Kooperativer Bibliotheksverbund

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Format: 1 online resource (430 pages)

Edition: 1st ed.

ISBN: 1-80064-858-8

Note: Intro -- Preface: A Vision of Transformed Conservation Practice -- References -- List of Authors -- Acknowledgements -- Reference -- PART I: WHAT IS THE PROBLEM? -- 1. Introduction: The Evidence Crisis and the Evidence Revolution -- 1.1 The Aim of the Book -- 1.2 The Evidence Crisis -- 1.3 Why is Poor Decision Making So Common? -- 1.4 The Evidence Revolution -- 1.5 The Case for Adopting Evidence Use -- 1.6 The Inefficiency Paradox -- 1.7 Transforming Decision Making -- 1.8 Structure of the Book -- References -- PART II: OBTAINING, ASSESSING AND SUMMARISING EVIDENCE -- 2. Gathering and Assessing Pieces of Evidence -- 2.1 What Counts as Evidence? -- 2.2 A Framework for Assessing the Weight of Evidence -- 2.3 Weighing the Evidence -- 2.4 Subjects of Evidence -- 2.5 Sources of Evidence -- 2.6 Types of Evidence -- 2.7 Acknowledgements -- References -- 3. Assessing Collated and Synthesised Evidence -- 3.1 Collating the Evidence -- 3.2 Systematic Maps -- 3.3 Subject-Wide Evidence Syntheses -- 3.4 Systematic Reviews -- 3.5 Rapid Evidence Assessments -- 3.6 Meta-Analyses -- 3.7 Open Access Effect Sizes -- 3.8 Overviews of Reviews -- References -- 4. Presenting Conclusions from Assessed Evidence -- 4.1 Principles for Presenting Evidence -- 4.2 Describing Evidence Searches -- 4.3 Presenting Different Types of Evidence -- 4.4 Presenting Evidence Quality -- 4.5 Balancing Evidence of Varying Strength -- 4.6 Visualising the Balance of Evidence -- 4.7 Synthesising Multiple Evidence Sources -- References -- 5. Improving the Reliability of Judgements -- 5.1 The Role of Judgements in Decision-Making -- 5.2 When Experts Are Good (and Not so Good) -- 5.3 Blind Spots of the Human Mind -- 5.4 Strategies for Improving Judgements -- 5.5 Structured Frameworks for Making Group Judgements -- 5.6 Practical Methods for Improving Routine Judgements -- References. , PART III: MAKING AND APPLYING DECISIONS -- 6. Identifying Stakeholders and Collaborating with Communities -- 6.1 The Benefits of Community-Working -- 6.2 Types of Community Engagement -- 6.3 Identifying Who to Collaborate With -- 6.4 Initiating Contact -- 6.5 Creating and Maintaining Trust -- 6.6 Collaborating -- References -- 7. Framing the Problem and Identifying Potential Solutions -- 7.1 The Approach to Identifying Problems and Potential Solutions -- 7.2 Defining the Scope of the Project and the Conservation Targets -- 7.3 Understanding the Biological and Human System -- 7.4 Identifying Threats and Opportunities -- 7.5 Taking Stock -- 7.6 Identifying Potential Actions -- 7.7 Developing Questions and Assumptions -- References -- 8 Making Decisions for Policy and Practice -- 8.1 What is a Structured Approach to Decision-Making? -- 8.2 Filter Easy Decisions: Deciding Whether to Invest in Decision Making -- 8.3 Preparing to Make the Decision -- 8.4 Making Decisions -- 8.5 Multi-Criteria Analysis -- 8.6 Strategy Table -- 8.7 Classifying Decisions -- 8.8 Decision Trees -- 8.9 Creating Models -- 8.10 Achieving Consensus -- References -- 9. Creating Evidence-Based Policy and Practice -- 9.1 How Embedding Evidence Improves Processes -- 9.2 General Principles for Embedding Evidence into Processes -- 9.3 Evaluating Evidence Use -- 9.4 Evidence-Based Species and Habitat Management Plans -- 9.5 Evidence-Based Guidance -- 9.6 Evidence-Based Policy -- 9.7 Evidence-Based Business Decisions -- 9.8 Evidence-Based Writing and Journalism -- 9.9 Evidence-Based Funding -- 9.10 Evidence-Based Decision-Support Tools -- 9.11 Evidence-Based Models -- References -- 10. How Conservation Practice Can Generate Evidence -- 10.1 Ensuring Data Collection is Useful -- 10.2 Collecting Data Along the Causal Chain -- 10.3 Incorporating Tests into Conservation Practice. , 10.4 Design of Experiments and Tests -- 10.5 Value of Information: When Do We Know Enough? -- 10.6 Writing Up and Sharing Results -- References -- PART IV: TRANSFORMING SOCIETY -- 11. Creating a Culture of Evidence Use -- 11.1 Why Changing Cultures is Critical -- 11.2 Auditing Current Evidence Use -- 11.3 Creating an Evidence-Use Plan -- 11.4 Creating Expectations and Opportunities for Evidence Use -- 11.5 Providing the Capacity to Deliver Evidence Use -- 11.6 Training, Capacity Building, and Certification -- 11.7 Learning from Failure -- 11.8 Case Studies: Organisations who Shifted to Embrace Evidence Use -- References -- 12. Transforming Practice: Checklists for Delivering Change -- 12.1 The Importance of Checklists -- 12.2 The Decision-Making Process -- 12.3 Organisations -- 12.4 Knowledge Brokers -- 12.5 Practitioners and Decision Makers -- 12.6 Commissioners of Reports and Advice -- 12.7 Funders and Philanthropists -- 12.8 The Research and Education Community -- References -- 13. Supplementary Material from Online Resources -- 13.1 Sources of Evidence -- 13.2 Teaching Evidence Use -- 13.3 Building the Evidence Base -- 13.4 Delivering Change -- 13.5 Collaborators -- References -- Checklists, Boxes and Tables -- Checklists -- Boxes -- Tables -- Figures -- Index.

Language: English

Keywords: Electronic books.

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Format: 1 online resource (282 pages) : , digital, PDF file(s).

Edition: Second edition.

ISBN: 1-139-17332-4

Content: This is a new edition of a very successful introduction to statistical methods for general insurance practitioners. No prior statistical knowledge is assumed, and the mathematical level required is approximately equivalent to school mathematics. Whilst the book is primarily introductory, the authors discuss some more advanced topics, including simulation, calculation of risk premiums, credibility theory, estimation of outstanding claim provisions and risk theory. All topics are illustrated by examples drawn from general insurance, and references for further reading are given. Solutions to most of the exercises are included. For the new edition the opportunity has been taken to make minor improvements and corrections throughout the text, to rewrite some sections to improve clarity, and to update the examples and references. A new section dealing with estimation has also been added.

Note: Title from publisher's bibliographic system (viewed on 05 Oct 2015). , Cover -- Half-title -- Title -- Copyright -- Contents -- Preface to first edition -- Preface to second edition -- Introduction and mathematical preliminaries -- 1.1 Introduction -- 1.2 Summation notation -- 1.3 Factorial notation n! -- 1.4 Combinatorial notation -- 1.5 Power notation -- 1.6 Differentiation -- the slope of a curve -- 1.7 Maxima and minima -- *1.8 Functions of more than one variable -- maxima and minima -- 1.9 The exponential function e x -- 1.10 The natural logarithm function In x -- 1.11 Exercises -- Elementary probability -- 2.1 Introduction -- concept of probability -- 2.2 Joint and disjoint events -- intersection and union -- 2.3 Conditional probability -- 2.4 Independence of two events -- 2.5 Exercises -- Random variables and their distributions -- 3.1 Discrete random variables and their distributions -- 3.2 Continuous random variables and their distributions -- 3.3 The area under a curve -- integration and differentiation -- 3.4 Exercises -- Location and dispersion -- 4.1 Measures of location - mean, median and mode -- 4.2 Dispersion - variance and standard deviation -- **4.3 Expectations and moments -- **4.4 Conditional means -- **4.5 Conditional variances -- **4.6 Skewness -- 4.7 Exercises -- Statistical distributions useful in general insurance work -- 5.1 The normal distribution -- 5.2 The Central Limit Theorem -- 5.3 The log-normal distribution -- *5.4 The Pareto distribution -- *5.5 The gamma distribution -- 5.6 The Poisson distribution -- 5.7 Normal approximation to the Poisson distribution -- *5.8 The binomial distribution -- **5.9 The negative binomial distribution -- heterogeneity of risk -- 5.10 The importance of theoretical distributions in general insurance -- Exercises -- Inferences from general insurance data -- 6.1 Hypothesis testing -- 6.2 Point estimation and method of moments -- *6.3 Maximum likelihood. , *6.4 Confidence intervals -- *6.5 Risk factors -- multivariate models -- least squares -- 6.6 Exercises -- The risk premium -- 7.1 Risk premium -- claim frequency and claim size -- 7.2 Claim frequency rate -- exposure -- 7.3 Claim size -- pitfalls -- 7.4 Claim settlement pattern -- *7.5 Excesses and excess of loss reinsurances -- 7.6 Exercises -- Experience rating -- 8.1 Introduction -- 8.2 Credibility theory -- 8.3 Full credibility -- 8.4 Partial credibility -- *8.5 Bayes' Theorem -- **8.6 A Bayesian approach to the updating of claim frequency rates -- 8.7 No claim discount (NCD) -- Exercises -- Simulation -- 9.1 Random numbers and simulation -- *9.2 How many simulations? -- 9.3 Computer generation of random numbers -- *9.4 Linear congruential generators -- 9.5 Random observations on the normal distribution -- 9.6 Random observations on the log-normal distribution -- 9.7 Random observations on the Poisson distribution -- *9.8 Random observations on the negative binomial distribution -- 9.9 A simulation example -- 9.10 When to simulate -- 9.11 Simulation of an NCD system -- 9.12 Limitations of the model -- sensitivity analysis -- 9.13 Exercises -- Estimation of outstanding claim provisions -- 10.1 Delays in claim reporting and claim settlement -- run-off -- 10.2 The run-off triangle -- 10.3 Chain-ladder method without inflation adjustment -- 10.4 Does the chain-ladder model fit the data? -- 10.5 Chain-ladder method with inflation adjustment -- 10.6 The separation method (direct future payments approach) -- *10.7 The separation method (two other approaches) -- 10.8 IBNR, and the chain-ladder and separation methods -- 10.9 Alternative methods of assessing outstanding claim provisions -- 10.10 The tail -- 10.11 Estimation of IBNR claim provisions -- 10.12 Exercises -- Elementary risk theory -- 11.1 Introduction. , 11.2 Portfolio with constant (fixed) claim size -- 11.3 Variable claim size -- **11.4 The expectation and variance of C -- 11.5 The assumption of normality -- 11.6 Summary and further reading -- 11.7 Exercises -- References -- Solutions to exercises -- Author index -- Subject index. , English

Additional Edition: ISBN 0-521-65534-X

Additional Edition: ISBN 0-521-65234-0

Language: English

URL: Volltext  (lizenzpflichtig)

URL: Inhaltsverzeichnis

URL: Inhaltstext

URL: https://doi.org/10.1017/CBO9781139173322

URL: https://doi.org/10.1017/CBO9781139173322

UID:

Format: 1 online resource (xiv, 238 pages) : , digital, PDF file(s).

ISBN: 1-139-17111-9

Content: Anthropology students increasingly need a quantitative background, but statistics are often seen as difficult and impenetrable. Statistics for Anthropology offers students of anthropology and other social sciences an easy, step-by-step route through the statistical maze. In clear, simple language, using relevant examples and practice problems, it provides a solid footing in basic statistical techniques, and is designed to give students a thorough grounding in methodology, and also insight into how and when to apply the various processes. The book assumes a minimal background in mathematics, and is suitable for the computer-literate and illiterate. Although only a hand calculator is needed, computer statistical software can be used to accompany the text. This book will be a 'must-have' for all anthropology and social science students needing an introduction to basic statistics.

Note: Title from publisher's bibliographic system (viewed on 05 Oct 2015). , Cover -- Half-title -- Title -- Copyright -- Dedication -- Contents -- Preface -- Introduction to statistics -- 1.1 Statistics and scientific inquiry -- 1.2 Basic definitions -- 1.3 Statistical notation -- 1.4 Rounding-off rules -- 1.5 Key concepts -- 1.6 Exercises -- Frequency distributions and graphs -- 2.1 Frequency distributions of qualitative variables -- 2.2 Frequency distributions of numerical discontinuous variables -- 2.3 Frequency distributions of continuous numerical variables -- 2.4 Graphs -- 2.5 Key concepts -- 2.6 Exercises -- Descriptive statistics: measures of central tendency and dispersion -- 3.1 Measures of central tendency -- 3.2 Measures of variation -- 3.3 A research example of descriptive statistics -- 3.4 Key concepts -- 3.5 Exercises -- Probability and statistics -- 4.1 Random sampling and probability distributions -- 4.2 The probability distribution of qualitative and discontinuous numerical variables -- 4.3 The binomial distribution -- 4.4 The probability distribution of continuous variables -- 4.5 The probability distribution of sample means -- 4.6 A research example of z scores -- 4.7 Key concepts -- 4.8 Exercises -- Hypothesis testing -- 5.1 The principles of hypothesis testing -- 5.2 Errors and power in hypothesis testing -- 5.3 Examples of hypothesis tests using z scores -- 5.4 One- and two-tail hypothesis tests -- 5.5 Assumptions of statistical tests -- 5.6 Hypothesis testing with the t distribution -- 5.7 Examples of hypothesis tests with t scores -- 5.8 Reporting hypothesis tests -- 5.9 Key concepts -- 5.10 Exercises -- The difference between two means -- 6.1 The un-paired t test -- 6.2 Assumptions of the un-paired t test -- 6.3 A research example of the un-paired t test -- 6.4 The comparison of a single observation with the mean of a sample -- 6.5 The comparison of paired samples. , 6.6 Assumptions of the paired t test -- 6.7 A research example of the paired t test -- 6.8 Key concepts -- 6.9 Exercises -- Analysis of variance (ANOVA) -- 7.1 One-way ANOVA -- 7.2 ANOVA procedure and nomenclature -- 7.3 ANOVA assumptions -- 7.4 Post ANOVA comparison of means -- 7.5 A research example of an ANOVA -- 7.6 Key concepts -- 7.7 Exercises -- Non-parametric comparison of samples -- 8.1 Ranking data -- 8.2 The Mann-Whitney U test for an un-matched design -- 8.3 A research example of the Mann-Whitney U test -- 8.4 The Kruskal-Wallis instead of a one-way, model I ANOVA -- 8.5 A research example of the Kruskal-Wallis test -- 8.6 The Wilcoxon signed-rank test for a paired design -- 8.7 A research example of the use of the Wilcoxon signed-rank test -- 8.8 Key concepts -- 8.9 Exercises -- Simple linear regression -- 9.1 An overview of regression analysis -- 9.2 Plot and inspection of the data -- 9.3 Description of the relation between X and y with an equation -- 9.4 Expression of the regression analysis as an analysis of variance of Y -- 9.5 Test of the null hypothesis H0: β=0 -- 9.6 Use of the regression equation to predict values of Y -- 9.7 Residual analysis -- 9.8 A research example of the use of regression -- 9.9 Key concepts -- 9.10 Exercises -- Correlation analysis -- 10.1 The Pearson product-moment correlation -- 10.2 A research example of the use of Pearson correlation -- 10.3 The Spearman correlation -- 10.4 A research example of the Spearman correlation coefficient -- 10.5 Key concepts -- 10.6 Exercises -- The analysis of frequencies -- 11.1 The X2 test for goodness-of-fit -- 11.2 A research example of the X2 test for goodness-of-fit -- 11.3 The X2 test for independence of variables -- 11.4 A research example of the X2 test for independence of variables -- 11.5 Yates' correction for continuity -- 11.6 Key concepts -- 11.7 Exercises. , References -- Answers to selected exercises -- A brief overview of SAS/ASSIST -- Statistical tables -- Table 1. The unit normal table -- Table 2. Critical values of the t distribution -- Table 3. Upper 5 and 1% points of the maximum F-rati -- Table 4. Critical values of the F distribution -- Table 5. Critical values of U, the Mann-Whitney statistic -- Table 6. Critical values of the chi-square distribution -- Table 7. Critical values of T for the Wilcoxon signed-rank test -- Table 8. Critical values of the Pearson correlation coefficient r -- Index. , English

Additional Edition: ISBN 0-521-57786-1

Additional Edition: ISBN 0-521-57116-2

Language: English

URL: Volltext  (lizenzpflichtig)

URL: https://doi.org/10.1017/CBO9781139171113

URL: https://doi.org/10.1017/CBO9781139171113

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Format: xiv, 546 Seiten : , Diagramme ; , 254 mm.

Edition: Second edition

ISBN: 978-1-4625-4986-3

Series Statement: Methodology in the Social Sciences

Content: The most user-friendly and authoritative resource on missing data has been completely revised to make room for the latest developments that make handling missing data more effective. The second edition includes new methods based on factored regressions, newer model-based imputation strategies, and innovations in Bayesian analysis. State-of-the-art technical literature on missing data is translated into accessible guidelines for applied researchers and graduate students. The second edition takes an even, three-pronged approach to maximum likelihood estimation (MLE), Bayesian estimation as an alternative to MLE, and multiple imputation. Consistently organized chapters explain the rationale and procedural details for each technique and illustrate the analyses with engaging worked-through examples on such topics as young adult smoking, employee turnover, and chronic pain. The companion website (www.appliedmissingdata.com) includes datasets and analysis examples from the book, up-to-date software information, and other resources.New to This Edition*Expanded coverage of Bayesian estimation, including a new chapter on incomplete categorical variables.*New chapters on factored regressions, model-based imputation strategies, multilevel missing data-handling methods, missing not at random analyses, and other timely topics.*Presents cutting-edge methods developed since the 2010 first edition; includes dozens of new data analysis examples.*Most of the book is entirely new.

Note: References S. 493-517, Author Index S. 519-528, Subject Index S. 529-545 , 1. Introduction to Missing Data; 1.1 Chapter Overview; 1.2 Missing Data Patterns; 1.3 Missing Data Mechanisms; 1.4 Diagnosing Missing Data Mechanisms; 1.5 Auxiliary Variables; 1.6 Analysis Example: Preparing for Missing Data Handling; 1.7 Older Missing Data Methods; 1.8 Comparing Missing Data Methods via Simulation; 1.9 Planned Missing Data; 1.10 Power Analyses for Planned Missingness Designs; 1.11 Summary and Recommended Readings; 2.- , Maximum Likelihood Estimation; 2.1 Chapter Overview; 2.2 Probability Distributions versus Likelihood Functions; 2.3 The Univariate Normal Distribution; 2.4 Estimating Unknown Parameters; 2.5 Getting an Analytic Solution; 2.6 Estimating Standard Errors; 2.7 Information Matrix and Parameter Covariance Matrix; 2.8 Alternative Approaches to Estimating Standard Errors; 2.9 Iterative Optimization Algorithms; 2.10 Linear Regression; 2.11 Significance Tests; 2.12 Multivariate Normal Data; 2.13 Categorical Outcomes: Logistic and Probit Regression; 2.14 Summary and Recommended Readings; 3.- , Maximum Likelihood Estimation with Missing Data; 3.1 Chapter Overview; 3.2 The Multivariate Normal Distribution Revisited; 3.3 How Do Incomplete Data Records Help?; 3.4 Standard Errors with Incomplete Data; 3.5 The Expectation Maximization Algorithm; 3.6 Linear Regression; 3.7 Significance Testing; 3.8 Interaction Effects; 3.9 Curvilinear Effects; 3.10 Auxiliary Variables; 3.11 Categorical Outcomes; 3.12 Summary and Recommended Readings; 4. Bayesian Estimation; 4.1 Chapter Overview; 4.2 What Makes Bayesian Statistics Different?; 4.3 Conceptual Overview of Bayesian Estimation; 4.4 Bayes’ Theorem; 4.5 The Univariate Normal Distribution; 4.6 MCMC Estimation with the Gibbs Sampler; 4.7 Estimating the Mean and Variance with MCMC; 4.8 Linear Regression; 4.9 Assessing Convergence of the Gibbs Sampler; 4.10 Multivariate Normal Data; 4.11 Summary and Recommended Readings; 5.- , Bayesian Estimation with Missing Data; 5.1 Chapter Overview; 5.2 Imputing an Incomplete Outcome Variable; 5.3 Linear Regression; 5.4 Interaction Effects; 5.5 Inspecting Imputations; 5.6 The Metropolis–Hastings Algorithm; 5.7 Curvilinear Effects; 5.8 Auxiliary Variables; 5.9 Multivariate Normal Data; 5.10 Summary and Recommended Readings; 6. Bayesian Estimation for Categorical Variables; 6.1 Chapter Overview; 6.2 Latent Response Formulation for Categorical Variables; 6.3 Regression with a Binary Outcome; 6.4 Regression with an Ordinal Outcome; 6.5 Binary and Ordinal Predictor Variables; 6.6 Latent Response Formulation for Nominal Variables; 6.7 Regression with a Nominal Outcome; 6.8 Nominal Predictor Variables; 6.9 Logistic Regression; 6.10 Summary and Recommended Readings; 7.- , Multiple Imputation; 7.1 Chapter Overview; 7.2 Agnostic versus Model-Based Multiple Imputation; 7.3 Joint Model Imputation; 7.4 Fully Conditional Specification; 7.5 Analyzing Multiply-Imputed Data Sets; 7.6 Pooling Parameter Estimates; 7.7 Pooling Standard Errors; 7.8 Test Statistic and Confidence Intervals; 7.9 When Might Multiple Imputation Give Different Answers?; 7.10 Interaction and Curvilinear Effects Revisited; 7.11 Model-Based Imputation; 7.12 Multivariate Significance Tests; 7.13 Summary and Recommended Readings; 8. Multilevel Missing Data; 8.1 Chapter Overview; 8.2 Random Intercept Regression Models; 8.3 Random Coefficient Models; 8.4 Multilevel Interaction Effects; 8.5 Three-Level Models; 8.6 Multiple Imputation; 8.7 Joint Model Imputation; 8.8 Fully Conditional Specification Imputation; 8.9 Maximum Likelihood Estimation; 8.10 Summary and Recommended Readings; 9.- , Missing Not at Random Processes; 9.1 Chapter Overview; 9.2 Missing Not at Random Processes Revisited; 9.3 Major Modeling Frameworks; 9.4 Selection Models for Multiple Regression; 9.5 Model Comparisons and Individual Influence Diagnostics; 9.6 Selection Model Analysis Examples; 9.7 Pattern Mixture Models for Multiple Regression; 9.8 Pattern Mixture Model Analysis Examples; 9.9 Longitudinal Data Analyses; 9.10 Diggle–Kenward Selection Model; 9.11 Shared Parameter (Random Coefficient) Selection Model; 9.12 Random Coefficient Pattern Mixture Models; 9.13 Longitudinal Data Analysis Examples; 9.14 Summary and Recommended Readings; 10.- , Special Topics and Applications; 10.1 Chapter Overview; 10.2 Descriptive Summaries, Correlations, and Subgroups; 10.3 Non-Normal Predictor Variables; 10.4 Non-Normal Outcome Variables; 10.5 Mediation and Indirect Effects; 10.6 Structural Equation Models; 10.7 Scale Scores and Missing Questionnaire Items; 10.8 Interactions with Scales; 10.9 Longitudinal Data Analyses; 10.10 Regression with a Count Outcome; 10.11 Power Analyses for Growth Models with Missing Data; 10.12 Summary and Recommended Readings; 11. Wrap-Up; 11.1 Chapter Overview; 11.2 Choosing a Missing Data-Handling Procedure; 11.3 Software Landscape; 11.4 Reporting Results from a Missing Data Analysis; 11.5 Final Thoughts and Recommended Readings; Appendix. Data Set Descriptions; Author Index; Subject Index; About the Author;

Language: English

Subjects: Psychology , Sociology

Keywords: Sozialwissenschaften ; Statistik ; Methodologie ; Fehlende Daten ; Datenauswertung

URL: Inhaltsverzeichnis

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Format: 1 online resource (xiii, 381 pages) : , digital, PDF file(s).

ISBN: 1-108-61724-7 , 1-108-62022-1 , 1-108-75507-0

Series Statement: Educational and psychological testing in a global context

Content: To look forward, it is necessary to look back and learn. History is more than just facts about the past; it is a narrative told from a particular perspective. A proverb from Africa, 'Until lions have their own historians, tales of the hunt shall always glorify the hunter,' captures this best. Most of the scholarship about psychological assessment comes from very specific nationalities and cultures, which does not truly reflect the diversity and breadth of histories pertaining to the field. Covering 50 countries, this collection gives voice to those that have previously been under represented and sometimes marginalized. This book not only describes important moments in psychological assessment from around the globe, but also equips readers with the tools to map the future of psychological assessment across nations. It advocates for a more globally inclusive science of assessment that holds promise for enhancing creativity and innovation in the field.

Note: Title from publisher's bibliographic system (viewed on 01 Aug 2022). , Cover -- Half-title -- Series information -- Title page -- Copyright information -- Contents -- List of Tables -- List of Contributors -- Series Editor's Foreword -- Chapter 1 Histories of Psychological Assessment: An Introduction -- 1.1 Introduction -- 1.2 Overview of the Book -- 1.3 Africa -- 1.4 The Arab Regions -- 1.5 Europe -- 1.6 Asia -- 1.7 Oceania -- 1.8 The Americas -- 1.9 Conclusion -- References -- Chapter 2 Psychological Assessment in Southern Africa -- 2.1 Introduction -- 2.2 Psychological Assessment in South Africa -- 2.3 Psychological Assessment in Botswana -- 2.3.1 Projective Tests Study -- 2.3.2 Regional Comparative or Benchmark Studies -- 2.3.3 Policy Formulation and Support Studies -- 2.4 Psychological Assessment in Zimbabwe -- 2.5 Psychological Assessment in Zambia -- 2.6 Conclusion -- References -- Chapter 3 Psychological Testing and Inclusive Schooling: Issues and Prospects in Central Africa -- 3.1 Introduction -- 3.2 Types of Psychological Tests and Inclusive Schooling in Central Africa -- 3.3 Issues/Problems Associated with the Current State of Psychological Testing in Central Africa -- 3.4 Prospects of Psychological Testing and School Reforms in Central Africa -- 3.5 Conclusion -- References -- Chapter 4 Psychological Assessment in West Africa -- 4.1 Introduction -- 4.2 Psychological Assessment in Ghana -- 4.3 Psychological Assessment in Nigeria -- 4.4 Psychological Assessment in Liberia and Sierra Leone -- 4.5 Current Challenges -- 4.6 Future Prospects -- 4.7 Conclusion -- References -- Chapter 5 Psychological Assessment in the Levant -- 5.1 Introduction -- 5.1.1 Historical Development -- 5.2 Psychological Assessment Research in Levant -- 5.2.1 Jordan -- 5.2.2 Lebanon -- 5.2.3 Palestine -- 5.2.4 Syria -- 5.3 The Current Impact of Sociocultural Factors on Assessment -- 5.3.1 Arabic Language -- 5.3.2 Bilingualism. , 5.3.3 Intellectual Dependency -- 5.3.4 Economic and Sociopolitical Context -- 5.3.5 Education, Training, and Professional Regulation -- 5.3.6 Collaboration -- 5.4 Conclusion and Recommendations -- References -- Chapter 6 The History of Assessment in the Nordic Countries -- 6.1 Introduction -- 6.2 Scandinavian Psychology Prior to the World Wars -- 6.3 Psychology and Assessment in Scandinavia during the World Wars (1923-1945) -- 6.4 Scandinavian Psychology Post the World Wars -- 6.5 Scandinavian Psychological Assessment: 1960s-1970s -- 6.6 Psychological Assessment: 2000-Present -- 6.7 Conclusion -- References -- Chapter 7 Key Episodes in The History of Testing in Central Western Europe -- 7.1 Introduction -- 7.2 Wilhelm Wundt, Experimental Psychological Laboratories, and Early Testing -- 7.3 Alfred Binet and the Measurement of Intelligence -- 7.4 William Stern and the Foundation of Differential Psychology -- 7.5 Hugo Münsterberg and the First Selection Tests -- 7.6 Otto Lipmann, William Stern, Institutes of Applied Psychology and the First Journal of Applied Psychology -- 7.7 The Exodus from Germany -- 7.8 Wehrmachtpsychologie Testing -- 7.9 Dutch Psychometrics and the Centraal Instituut voor Toetsontwikkeling -- 7.10 Internationalization and Critical Psychology -- 7.11 University Entry Selection in Belgium, Germany, and the Netherlands -- 7.12 Modern Use of Tests in Organizations -- 7.13 Outlook and Trends -- References -- Chapter 8 The Beginnings of Psychological Assessment in Spain and Portugal -- 8.1 Introduction -- 8.2 Huarte de San Juan, a Precursor -- 8.3 Psychological Assessment in the Nineteenth Century -- 8.4 Psychological Assessment in the Twentieth Century (1900-1949) -- 8.5 Further Developments in Psychological Assessment (1950-1969) -- 8.6 Psychological Assessment in Spain and Portugal (1970-1990). , 8.7 Psychological Assessment in Spain and Portugal Post 1990 -- 8.8 Conclusion -- References -- Chapter 9 Histories of Psychological Assessments in the United Kingdom -- 9.1 Introduction -- 9.2 The Early Development of Psychological Assessment (1859-1890) -- 9.3 From the 1890s to the Great War of 1914-1918 -- 9.4 The Development of Psychometrics -- 9.5 The Great 1914-1918 War -- 9.6 The Inter-war Years (1918-1939) -- 9.7 World War II and the Post-war Years (1940s-1970s) -- 9.8 The Development of Assessment Centers -- 9.9 The 1970s and 1980s: Assessment Centers in the Private Sector -- 9.10 Concerns over Bias and Fairness in Testing -- 9.11 The Cyril Burt Saga and the Continuing Eugenics Issue -- 9.12 The Growth in Use of Personality Assessment -- 9.13 The Growth of Occupational Assessment in the United Kingdom (1970s-1990s) -- 9.14 The Technological Revolution in the 1980s to the Late 1990s -- 9.15 Item Response Theory -- 9.16 The British Ability Scales -- 9.17 Item Generation and Test Generation -- 9.18 The Internet (Late 1990s to Now) -- 9.19 Conclusion -- References -- Chapter 10 The Early History of Psychological Testing in Eastern Europe and Russia -- 10.1 The General Context of Eastern Europe -- 10.2 Psychology in Eastern Europe: The Early Days -- 10.2.1 Russia -- 10.2.2 Romania -- 10.2.3 Poland -- 10.2.4 The Former Kingdom/Republic of Yugoslavia -- 10.2.5 Czechoslovakia -- 10.2.6 Hungary -- 10.3 The Dark Days -- 10.4 The Revival -- 10.5 Conclusion -- References -- Chapter 11 Hearing the Untold: A Review of Central Asia's Contribution to the Expansion of Psychological Assessment -- 11.1 Introduction -- 11.2 Iran -- 11.3 Pakistan -- 11.4 Afghanistan -- 11.5 Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, and Uzbekistan -- 11.6 Conclusion -- References -- Chapter 12 History of Psychological Assessment in Southern Asia -- 12.1 Introduction. , 12.2 Assessment in South Asia Pre-colonialism -- 12.3 Psychological Assessment in India -- 12.4 Assessment in Educational Settings -- 12.5 Assessment in Clinical Settings -- 12.6 Assessment in Social Settings -- 12.7 Assessment in Workplace Settings -- 12.8 Assessment in Military Settings -- 12.9 Psychological Assessment in Sri Lanka -- 12.10 Psychological Assessment in Bangladesh -- 12.11 Psychological Assessment in Nepal -- 12.12 Psychological Assessment in Bhutan -- 12.13 Conclusion -- References -- Chapter 13 The History of Psychological Testing in East Asia -- 13.1 Introduction -- 13.2 The History of Psychological Testing in China -- 13.2.1 The Idea and Practice of Psychological Testing in Ancient China -- 13.2.2 Development of Modern Psychological Testing in China (1915-1948) -- 13.2.3 Development of Psychological Testing in China after 1949 -- 13.2.4 cpai: One Representative Personality Test -- 13.3 Psychological Testing in Japan -- 13.4 Psychological Testing in South Korea -- 13.5 Challenges and Future Directions of Psychological Testing in East Asia -- References -- Chapter 14 Psychological Assessment and Testing in Malaysia and Singapore -- 14.1 Introduction -- 14.2 Contextualizing Malaysia -- 14.3 History of Psychology in Malaysia -- 14.4 The Development of Psychological Assessments in Malaysia -- 14.5 Contextualizing Singapore -- 14.6 History of Psychology in Singapore -- 14.7 The Development of Psychological Assessment in Singapore -- 14.8 Conclusion -- References -- Chapter 15 A Brief History of Testing and Assessment in Oceania -- 15.1 Introduction -- 15.2 Australia: Beginnings at the Turn of the Twentieth Century -- 15.3 World War I and the Interwar Years -- 15.4 World War II and the Postwar Years -- 15.5 Testing in Australia: 1960-1990 -- 15.6 Changes with the Coming of the Twenty-First Century. , 15.7 Recognizing Cultural Differences in Test Use -- 15.8 Aotearoa/New Zealand: The Early Years -- 15.9 Testing in Education, Industry, and the Clinics: 1940 Onwards -- 15.10 Testing in New Zealand: The Current Climate -- 15.11 Recognition of Cultural Differences -- 15.12 PNG and the Pacific Islands -- 15.13 Conclusion -- References -- Chapter 16 Psychological Assessment in South America: Perspectives from Brazil, Bolivia, Chile, and Peru -- 16.1 Introduction -- 16.2 Brazil -- 16.2.1 Historical Aspects of Test Use in Brazil -- 16.2.2 The Development of Psychological Assessment -- 16.2.3 Professional Regulations and Training -- 16.2.4 Increase of Research Groups and Production -- 16.2.5 Challenges and Vision of the Future -- 16.3 Bolivia -- 16.3.1 History of Psychological Assessment -- 16.3.2 Professional Training in Psychology -- 16.3.3 Assessment Practice -- 16.3.4 Challenges -- 16.4 Chile -- 16.4.1 Initial Historical Context -- 16.4.2 Deregulation and Attempts at Regulation -- 16.4.2.1 Training -- 16.4.2.2 Professional Practice -- 16.4.2.3 Research -- 16.4.3 Conclusions and Challenges for Assessment in Chile -- 16.5 Peru -- 16.5.1 History of Psychological Assessment in Peru -- 16.5.2 Training -- 16.5.3 Research -- 16.5.4 Challenges -- 16.6 Conclusion -- References -- Chapter 17 Historical Development of Psychological Assessment in the Caribbean -- 17.1 Introduction -- 17.2 Pre-Columbian Era to the Eighteenth Century -- 17.3 Slavery to the Nineteenth Century -- 17.4 Traditional Healers -- 17.5 Emancipation to the Twentieth Century -- 17.6 The Twenty-First Century and Cross-cultural Issues of Euro-American Assessment Methods -- 17.7 Conclusion -- Reference -- Chapter 18 The History of Psychological Assessment in North America -- 18.1 Introduction -- 18.2 Antecedents of Psychological Testing in North America -- 18.3 Intellectual Assessment. , 18.3.1 Adaptations of the Binet Scales.

Additional Edition: ISBN 1-108-48500-6

Language: English

URL: lizenzpflichtig

URL: https://doi.org/10.1017/9781108755078

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Format: 1 online resource (viii, 424 pages) : , digital, PDF file(s).

ISBN: 1-108-62032-9 , 1-108-61736-0 , 1-108-75552-6

Content: This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decomposition, the theory of random walks and Markov chains, the fundamentals of and important algorithms for machine learning, algorithms and analysis for clustering, probabilistic models for large networks, representation learning including topic modelling and non-negative matrix factorization, wavelets and compressed sensing. Important probabilistic techniques are developed including the law of large numbers, tail inequalities, analysis of random projections, generalization guarantees in machine learning, and moment methods for analysis of phase transitions in large random graphs. Additionally, important structural and complexity measures are discussed such as matrix norms and VC-dimension. This book is suitable for both undergraduate and graduate courses in the design and analysis of algorithms for data.

Note: Title from publisher's bibliographic system (viewed on 29 Jan 2020). , Cover -- Half-title -- Title page -- Copyright information -- Contents -- 1 Introduction -- 2 High-Dimensional Space -- 2.1 Introduction -- 2.2 The Law of Large Numbers -- 2.3 The Geometry of High Dimensions -- 2.4 Properties of the Unit Ball -- 2.4.1 Volume of the Unit Ball -- 2.4.2 Volume near the Equator -- 2.5 Generating Points Uniformly at Random from a Ball -- 2.6 Gaussians in High Dimension -- 2.7 Random Projection and Johnson-Lindenstrauss Lemma -- 2.8 Separating Gaussians -- 2.9 Fitting a Spherical Gaussian to Data -- 2.10 Bibliographic Notes -- 2.11 Exercises -- 3 Best-Fit Subspaces and Singular Value Decomposition (SVD) -- 3.1 Introduction -- 3.2 Preliminaries -- 3.3 Singular Vectors -- 3.4 Singular Value Decomposition (SVD) -- 3.5 Best Rank-k Approximations -- 3.6 Left Singular Vectors -- 3.7 Power Method for Singular Value Decomposition -- 3.7.1 A Faster Method -- 3.8 Singular Vectors and Eigenvectors -- 3.9 Applications of Singular Value Decomposition -- 3.9.1 Centering Data -- 3.9.2 Principal Component Analysis -- 3.9.3 Clustering a Mixture of Spherical Gaussians -- 3.9.4 Ranking Documents and Web Pages -- 3.9.5 An Illustrative Application of SVD -- 3.9.6 An Application of SVD to a Discrete Optimization Problem -- 3.10 Bibliographic Notes -- 3.11 Exercises -- 4 Random Walks and Markov Chains -- 4.1 Stationary Distribution -- 4.2 Markov Chain Monte Carlo -- 4.2.1 Metropolis-Hasting Algorithm -- 4.2.2 Gibbs Sampling -- 4.3 Areas and Volumes -- 4.4 Convergence of Random Walks on Undirected Graphs -- 4.4.1 Using Normalized Conductance to Prove Convergence -- 4.5 Electrical Networks and Random Walks -- 4.6 Random Walks on Undirected Graphs with Unit Edge Weights -- 4.6.1 Hitting Time -- 4.6.2 Commute Time -- 4.6.3 Cover Time -- 4.7 Random Walks in Euclidean Space -- 4.7.1 Random Walks on Lattices -- 4.7.2 Two Dimensions. , 4.7.3 Three Dimensions -- 4.8 The Web as a Markov Chain -- 4.8.1 Pagerank -- 4.8.2 Relation to Hitting Time -- 4.8.3 Spam -- 4.8.4 Personalized Pagerank -- 4.8.5 Algorithm for Computing Personalized Pagerank -- 4.9 Bibliographic Notes -- 4.10 Exercises -- 5 Machine Learning -- 5.1 Introduction -- 5.1.1 The Core Problem -- 5.1.2 How to Learn -- 5.2 The Perceptron Algorithm -- 5.3 Kernel Functions and Nonlinearly Separable Data -- 5.4 Generalizing to New Data -- 5.4.1 Overfitting and Uniform Convergence -- 5.4.2 Occam's Razor -- 5.4.3 Regularization: Penalizing Complexity -- 5.5 VC-Dimension -- 5.5.1 Definitions and Key Theorems -- 5.5.2 VC-Dimension of Some Set Systems -- 5.5.3 Shatter Function for Set Systems of Bounded VC-Dimension -- 5.5.4 VC-Dimension of Combinations of Concepts -- 5.5.5 The Key Theorem -- 5.6 VC-Dimension and Machine Learning -- 5.7 Other Measures of Complexity -- 5.8 Deep Learning -- 5.8.1 Generative Adversarial Networks (GANs) -- 5.9 Gradient Descent -- 5.9.1 Stochastic Gradient Descent -- 5.9.2 Regularizer -- 5.10 Online Learning -- 5.10.1 An Example: Learning Disjunctions -- 5.10.2 The Halving Algorithm -- 5.10.3 The Perceptron Algorithm -- 5.10.4 Inseparable Data and Hinge Loss -- 5.10.5 Online to Batch Conversion -- 5.10.6 Combining (Sleeping) Expert Advice -- 5.11 Boosting -- 5.12 Further Current Directions -- 5.12.1 Semi-Supervised Learning -- 5.12.2 Active Learning -- 5.12.3 Multitask Learning -- 5.13 Bibliographic Notes -- 5.14 Exercises -- 6 Algorithms for Massive Data Problems: Streaming, Sketching, and Sampling -- 6.1 Introduction -- 6.2 Frequency Moments of Data Streams -- 6.2.1 Number of Distinct Elements in a Data Stream -- 6.2.2 Number of Occurrences of a Given Element -- 6.2.3 Frequent Elements -- 6.2.4 The Second Moment -- 6.3 Matrix Algorithms Using Sampling -- 6.3.1 Matrix Multiplication Using Sampling. , 6.3.2 Implementing Length Squared Sampling in Two Passes -- 6.3.3 Sketch of a Large Matrix -- 6.4 Sketches of Documents -- 6.5 Bibliographic Notes -- 6.6 Exercises -- 7 Clustering -- 7.1 Introduction -- 7.1.1 Preliminaries -- 7.1.2 Two General Assumptions on the Form of Clusters -- 7.1.3 Spectral Clustering -- 7.2 k-Means Clustering -- 7.2.1 A Maximum-Likelihood Motivation -- 7.2.2 Structural Properties of the k-Means Objective -- 7.2.3 Lloyd's Algorithm -- 7.2.4 Ward's Algorithm -- 7.2.5 k-Means Clustering on the Line -- 7.3 k-Center Clustering -- 7.4 Finding Low-Error Clusterings -- 7.5 Spectral Clustering -- 7.5.1 Why Project? -- 7.5.2 The Algorithm -- 7.5.3 Means Separated by Ω(1) Standard Deviations -- 7.5.4 Laplacians -- 7.5.5 Local Spectral Clustering -- 7.6 Approximation Stability -- 7.6.1 The Conceptual Idea -- 7.6.2 Making This Formal -- 7.6.3 Algorithm and Analysis -- 7.7 High-Density Clusters -- 7.7.1 Single Linkage -- 7.7.2 Robust Linkage -- 7.8 Kernel Methods -- 7.9 Recursive Clustering Based on Sparse Cuts -- 7.10 Dense Submatrices and Communities -- 7.11 Community Finding and Graph Partitioning -- 7.12 Spectral Clustering Applied to Social Networks -- 7.13 Bibliographic Notes -- 7.14 Exercises -- 8 Random Graphs -- 8.1 The G(n,p) Model -- 8.1.1 Degree Distribution -- 8.1.2 Existence of Triangles in G(n, d/n) -- 8.2 Phase Transitions -- 8.3 Giant Component -- 8.3.1 Existence of a Giant Component -- 8.3.2 No Other Large Components -- 8.3.3 The Case of p < -- 1/n -- 8.4 Cycles and Full Connectivity -- 8.4.1 Emergence of Cycles -- 8.4.2 Full Connectivity -- 8.4.3 Threshold for O(ln n) Diameter -- 8.5 Phase Transitions for Increasing Properties -- 8.6 Branching Processes -- 8.7 CNF-SAT -- 8.7.1 SAT-Solvers in Practice -- 8.7.2 Phase Transitions for CNF-SAT -- 8.8 Nonuniform Models of Random Graphs. , 8.8.1 Giant Component in Graphs with Given Degree Distribution -- 8.9 Growth Models -- 8.9.1 Growth Model without Preferential Attachment -- 8.9.2 Growth Model with Preferential Attachment -- 8.10 Small-World Graphs -- 8.11 Bibliographic Notes -- 8.12 Exercises -- 9 Topic Models, Nonnegative Matrix Factorization, Hidden Markov Models, and Graphical Models -- 9.1 Topic Models -- 9.2 An Idealized Model -- 9.3 Nonnegative Matrix Factorization -- 9.4 NMF with Anchor Terms -- 9.5 Hard and Soft Clustering -- 9.6 The Latent Dirichlet Allocation Model for Topic Modeling -- 9.7 The Dominant Admixture Model -- 9.8 Formal Assumptions -- 9.9 Finding the Term-Topic Matrix -- 9.10 Hidden Markov Models -- 9.11 Graphical Models and Belief Propagation -- 9.12 Bayesian or Belief Networks -- 9.13 Markov Random Fields -- 9.14 Factor Graphs -- 9.15 Tree Algorithms -- 9.16 Message Passing in General Graphs -- 9.16.1 Graphs with a Single Cycle -- 9.16.2 Belief Update in Networks with a Single Loop -- 9.16.3 Maximum Weight Matching -- 9.17 Warning Propagation -- 9.18 Correlation between Variables -- 9.19 Bibliographic Notes -- 9.20 Exercises -- 10 Other Topics -- 10.1 Ranking and Social Choice -- 10.1.1 Randomization -- 10.1.2 Examples -- 10.2 Compressed Sensing and Sparse Vectors -- 10.2.1 Unique Reconstruction of a Sparse Vector -- 10.2.2 Efficiently Finding the Unique Sparse Solution -- 10.3 Applications -- 10.3.1 Biological -- 10.3.2 Low-Rank Matrices -- 10.4 An Uncertainty Principle -- 10.4.1 Sparse Vector in Some Coordinate Basis -- 10.4.2 A Representation Cannot Be Sparse in Both Time and Frequency Domains -- 10.5 Gradient -- 10.6 Linear Programming -- 10.6.1 The Ellipsoid Algorithm -- 10.7 Integer Optimization -- 10.8 Semi-Definite Programming -- 10.9 Bibliographic Notes -- 10.10 Exercises -- 11 Wavelets -- 11.1 Dilation -- 11.2 The Haar Wavelet. , 11.3 Wavelet Systems -- 11.4 Solving the Dilation Equation -- 11.5 Conditions on the Dilation Equation -- 11.6 Derivation of the Wavelets from the Scaling Function -- 11.7 Sufficient Conditions for the Wavelets to Be Orthogonal -- 11.8 Expressing a Function in Terms of Wavelets -- 11.9 Designing a Wavelet System -- 11.10 Applications -- 11.11 Bibliographic Notes -- 11.12 Exercises -- 12 Background Material -- 12.1 Definitions and Notation -- 12.1.1 Integers -- 12.1.2 Substructures -- 12.1.3 Asymptotic Notation -- 12.2 Useful Relations -- 12.3 Useful Inequalities -- 12.4 Probability -- 12.4.1 Sample Space, Events, and Independence -- 12.4.2 Linearity of Expectation -- 12.4.3 Union Bound -- 12.4.4 Indicator Variables -- 12.4.5 Variance -- 12.4.6 Variance of the Sum of Independent Random Variables -- 12.4.7 Median -- 12.4.8 The Central Limit Theorem -- 12.4.9 Probability Distributions -- 12.4.10 Bayes Rule and Estimators -- 12.5 Bounds on Tail Probability -- 12.5.1 Chernoff Bounds -- 12.5.2 More General Tail Bounds -- 12.6 Applications of the Tail Bound -- 12.7 Eigenvalues and Eigenvectors -- 12.7.1 Symmetric Matrices -- 12.7.2 Relationship between SVD and Eigen Decomposition -- 12.7.3 Extremal Properties of Eigenvalues -- 12.7.4 Eigenvalues of the Sum of Two Symmetric Matrices -- 12.7.5 Norms -- 12.7.6 Important Norms and Their Properties -- 12.7.7 Additional Linear Algebra -- 12.7.8 Distance between Subspaces -- 12.7.9 Positive Semi-Definite Matrix -- 12.8 Generating Functions -- 12.8.1 Generating Functions for Sequences Defined by Recurrence Relationships -- 12.8.2 The Exponential Generating Function and the Moment Generating Function -- 12.9 Miscellaneous -- 12.9.1 Lagrange Multipliers -- 12.9.2 Finite Fields -- 12.9.3 Application of Mean Value Theorem -- 12.10 Exercises -- References -- Index.

Additional Edition: Online version: Blum, Avrim, 1966- Foundations of data science New York, NY : Cambridge University Press, 2020. ISBN 9781108755528

Additional Edition: ISBN 9781108485067

Additional Edition: ISBN 1108485065

Language: English

URL: Volltext  (lizenzpflichtig)

URL: https://doi.org/10.1017/9781108755528

UID:

Format: 1 online resource (993 p.)

ISBN: 9786612253058 , 1-59124-988-0

Content: Does MEMS technology offer advantages to your company's products? Will miniature machines on a chip solve your application objectives for ôsmaller, better, cheaper, and faster'ö If you are a product development engineer or manager, the decision to design a MEMS device implies having an application and market. This book offers you a practical guide to making this important business decision. Here, both veterans and newcomers to MEMS device design will get advice on evaluating MEMS for their business, followed by guidance on selecting solutions, technologies and design support tools. You will se

Note: "Micro-electro-mechanical systems"--Cover. , Front Cover; MEMS: A Practical Guide to Design, Analysis, and Applications; Copyright Page; Contents; Foreword; Preface; Chapter 1. Microtransducer Operation; 1.1 Introduction; 1.2 Transduction; 1.3 Microsystem Performance; 1.4 Transducer Operation Techniques; 1.5 Powering Microsystems; References; Chapter 2. Material Properties: Measurement and Data; 2.1 Introduction; 2.2 Measurement Methods; 2.3 Data; References; Chapter 3. MEMS and NEMS Simulation; 3.1 Introduction; 3.2 Simulation Scenario; 3.3 Generic Organization of a Computational Tool; 3.4 Methods for Materials Simulation , 3.5 Computational Methods that Solve PDEs3.6 Design Automation Methods; 3.7 A Simulation Strategy; 3.8 Case Studies; 3.9 Summary; 3.10 Acknowledgments; References; Chapter 4. System-Level Simulation of Microsystems; 4.1 Introduction; 4.2 Behavioral Modeling of MEMS Components; 4.3 Formulation of Equations of Motion; 4.4 Structured Design Tools; 4.5 Conclusions; References; Chapter 5. Thermal-Based Microsensors; 5.1 Introduction; 5.2 Thermoresistors; 5.3 Silicon Diodes and Transistors as Thermal Microsensors; 5.4 Thermoelectric Microsensors , 5.5 CMOS-Compatible Thermal-Based Microsensors and Microactuators5.6 Diagnostic Thermal-Based Microstructures; 5.7 Conclusion; References; Chapter 6. Photon Detectors; 6.1 Introduction; 6.2 Detectors; 6.3 Thin-Film Transistors; 6.4 Pixel Integration; 6.5 Imaging Arrays; 6.6 New Challenges in Large-Area Digital Imaging; References; Chapter 7. Free-Space Optical MEMS; 7.1 Introduction; 7.2 General Discussion of Micromirror Scanners; 7.3 Electrostatic Scanners; 7.4 Scanning Mirrors with Magnetic and Electromagnetic Actuators; 7.5 Micromirror Arrays with Mirror Size =100 Micrometers , 9.5 Magnetoresistors9.6 Magnetodiodes; 9.7 Magnetotransistors and Related Microsensors; 9.8 Magnetic Field-based Functional Multisensors; 9.9 Interfaces and Improvement of Characteristics of Magnetic Microsensors; 9.10 Conclusions and Outlook; References; Chapter 10. Mechanical Microsensors; 10.1 Introduction; 10.2 Inertial Sensors; 10.3 Pressure Sensors; 10.4 Force and Torque Sensors; References; Chapter 11. Semiconductor-Based Chemical Microsensors; 11.1 Introduction; 11.2 Thermodynamics of Chemical Sensing; 11.3 Chemomechanical Sensors; 11.4 Thermal Sensors; 11.5 Optical Sensors , 11.6 Electrochemical Sensors , English

Additional Edition: ISBN 0-8155-1497-2

Language: English

UID:

Format: 1 online resource (261 pages) : , illustrations

ISBN: 0-08-101968-8 , 1-78548-200-9

Note: Front Cover -- Compressed Sensing in LiFi and WiFi Networks -- Copyright Page -- Contents -- Preface -- List of Acronyms -- Introduction -- 1. Shannon's Theorem in Classic Data Processing -- 1.1. Introduction -- 1.2. Shannon's theorem -- 1.3. Shannon's capacity channel -- 1.4. Sampling -- 1.5. Shannon's theorem in cryptography -- 2. Shannon's Theorem in Quantum Data -- 2.1. Introduction -- 2.2. Definition of quantum entropy -- 2.3. The Holevo bound -- 2.4. Quantum aspects of compressed sensing -- 2.5. Compressibility and capacity -- 2.6. Conclusion -- 3. Sparse Signals and Compressed Sensing -- 3.1. Introduction -- 3.2. Definition of sparse decomposition -- 3.3. Sparse signals -- 3.4. Compressed sensing and optimization -- 3.5. Reconstructing the sparse signal -- 4. Compressed Sensing and the Fourier Transform -- 4.1. Introduction -- 4.2. Fast SFT -- 4.3. Random matrices in compressed sensing -- 4.4. Conclusion -- 5. Compressed Sensing and Entanglement -- 5.1. Introduction -- 5.2. Uncertainty relationships -- 5.3. Phantom images -- 5.4. Sparsity in practice -- 5.5. Chaotic and quantum encryption software -- 6. [WITHDRAWN] Compressed Sensing and Intelligent LiFi Systems -- 7. Compressed Sensing in LiFi Systems in Mobile Communications and Cryptography -- 7.1. Introduction -- 7.2. Applications in digital communications: estimating the channel in OFDM systems -- 7.3. An application in cryptography: the SAC-OCDMA system -- 7.6. Conclusion -- 8. Compressed Sensing in WiFi Systems -- 8.1. Preliminaries on compressed sensing in WiFi systems -- 8.2. Interconnection of WiMAX mobile and satellite DVBS/ -- 9. Compressed Sensing in Interconnections Covering WiMAX, UMTS and MANET Satellite Networks -- 9.1. Introduction -- 9.2. Heterogeneous networks -- 9.3. Demands of heterogeneous networks -- 9.4. Other interconnection methods. , 9.5. Transparency in communications -- 9.6. Classes of service -- 9.7. Interconnection covering satellite, WiMAX, UMTS and MANET networks -- 9.8. The results -- 9.9. Discussion of results -- 9.10. Conclusion -- 10. Compressed Sensing in Radar Interferometry -- 10.1. Formatting inferograms -- 10.2. SWR synthesis -- 10.3. Geometric deformation -- 10.4. Re-sampling -- 10.5. Geometric interpretation of the registration -- 10.6. Simulated inferograms -- 10.7. Methods for minimizing norms -- 10.8. Conclusion -- 11. Compressed Sensing in Radars -- 11.1. Introduction -- 11.2. Description of the system -- 11.3. System analysis -- 11.4. Transmission channel -- 11.5. Decision variables -- 11.6. ANN-CFAR detector -- 11.7. Probability of detection and of false alarm -- 11.8. Mean acquisition time -- 11.9. Results and discussions -- 11.10. Conclusion -- 12. Compressed Sensing in Electromagnetism -- 12.1. Introduction -- 12.2. Compressed sensing in electromagnetism -- 12.3. Application of compressed sensing and electromagnetism: a solution for diffusion using an arbitrarily formed conductive structure by the moment method -- Conclusion -- Bibliography -- Index.

Language: English

UID:

Format: 1 online resource (417 p.)

Edition: 1st edition

ISBN: 1-283-70651-2 , 0-12-397773-8

Content: Energy is directly related to the most critical economic and social issues which affect sustainable development such as mobility, food production, environmental quality, regional and global security issues. Two-thirds of the new demand will come from developing nations, with China accounting for 30%. Without adequate attention to the critical importance of energy to all these aspects, the global, social, economic and environmental goals of sustainability cannot be achieved. Indeed the magnitude of change needed is immense, fundamental and directly related to the energy produced and consumed na

Note: Description based upon print version of record. , Front Cover; Distributed Renewable Energies For Off-Grid Communities: Strategies and Technologies toward Achieving Sustainability in Energy Generation and Supply; Copyright; Contents; Preface; List of Figures; List of Tables; Chapter One- Scope of the Book; 1.1.DISTRIBUTED ENERGY GENERATION; 1.2.DISTRIBUTED ENERGY SUPPLY; 1.3.COMMUNITY POWER; 1.4.OFF-GRID SYSTEMS; REFERENCES; Chapter Two- Restructuring Future Energy Generation and Supply; 2.1.BASIC CHALLENGES; 2.2.CURRENT ENERGY SUPPLIES; 2.3.PEAK OIL; 2.4.AVAILABILITY OF ALTERNATIVE RESOURCES; REFERENCES , Chapter Three- Road Map of Distributed Renewable Energy Communities3.1.ENERGY AND SUSTAINABLE DEVELOPMENT; 3.2.COMMUNITY INVOLVEMENT; 3.3.FACING THE CHALLENGES; 3.4.THE CONCEPT OF FAO, UN INTEGRATED ENERGY COMMUNITIES (IEC); 3.5.GLOBAL APPROACH; 3.6.BASIC AND EXTENDED NEEDS; 3.7.TYPICAL ELECTRICITY DEMANDS; 3.8.SINGLE AND MULTIPLE-PHASE ISLAND GRID; 3.9.REGIONAL IMPLEMENTATION; REFERENCES; FURTHER READING; Chapter Four- Planning of Integrated Renewable Communities; 4.1.SCENARIO 1; 4.2.SCENARIO 2; 4.3.CASE STUDY I: IMPLEMENTATION OF IEF UNDER CLIMATIC CONDITIONS OF CENTRAL EUROPE , 4.4.CASE STUDY II: ARID AND SEMI-ARID REGIONSREFERENCE; Chapter Five- Determination of Community Energy and Food Requirements; 5.1.MODELING APPROACHES; 5.2.DATA ACQUISITION; 5.3.DETERMINATION OF ENERGY AND FOOD REQUIREMENTS; 5.4.ENERGY POTENTIAL ANALYSIS; 5.5.DATA COLLECTION AND PROCESSING FOR ENERGY UTILIZATION; 5.6.WIND ENERGY; 5.7.BIOMASS; REFERENCES; Chapter Six- Energy Basics, Resources, Global Contribution and Applications; 6.1.BASICS OF ENERGY; 6.2.GLOBAL CONTRIBUTION; 6.3.RESOURCES AND APPLICATIONS; REFERENCES; Chapter Seven- Solar Energy; 7.1.PHOTOVOLTAIC , 7.2.CONCENTRATING SOLAR THERMAL POWER (CSP)7.3.SOLAR THERMAL COLLECTORS; 7.4.SOLAR COOKERS AND SOLAR OVENS; REFERENCES; Chapter Eight- Wind Energy; 8.1.GLOBAL MARKET; 8.2.TYPES OF WIND TURBINES; 8.3.SMALL WIND TURBINES; 8.4.GOOGLE SUPERHIGHWAY, USA; REFERENCES; Chapter Nine- Biomass and Bioenergy; 9.1.CHARACTERISTICS AND POTENTIALS; 9.2.SOLID BIOFUELS; 9.3.CHARCOAL; 9.4.BRIQUETTES; 9.5.PELLETS; 9.6.BIOGAS; 9.7.ETHANOL; 9.8.BIO-OILS; 9.9.CONVERSION SYSTEMS TO HEAT, POWER AND ELECTRICITY; 9.10.COMBINED HEAT AND POWER (CHP); 9.11.STEAM TECHNOLOGY; 9.12.GASIFICATION; 9.13.PYROLYSIS; 9.14.METHANOL , 9.15.SYNTHETIC OIL9.16.FUEL CELLS; 9.17.THE STIRLING ENGINE; 9.18.ALGAE; 9.19.HYDROGEN; REFERENCES; FURTHER READING; Chapter Ten- Hydropower; 10.1.HYDROELECTRICITY; 10.2.MICROHYDROPOWER SYSTEMS; 10.3.TURBINE TYPES; 10.4.POTENTIAL FOR RURAL DEVELOPMENT; REFERENCES; Chapter Eleven- Marine Energy; 11.1.OCEAN THERMAL ENERGY CONVERSION; 11.2.TECHNOLOGIES; 11.3.OCEAN TIDAL POWER; 11.4.OCEAN WAVE POWER; 11.5.ENVIRONMENTAL AND ECONOMIC CHALLENGES; REFERENCES; Chapter Twelve- Geothermal Energy; 12.1.ORIGIN OF GEOTHERMAL HEAT; 12.2.GEOTHERMAL ELECTRICITY; 12.3.TYPES OF GEOTHERMAL POWER PLANTS , REFERENCES , English

Additional Edition: ISBN 0-12-397178-0

Language: English

UID:

Format: 1 online resource (461 p.)

Edition: 2nd ed.

ISBN: 1-281-05438-0 , 9786611054380 , 0-08-051912-1

Content: This text is a self-contained Second Edition, providing an introductory account of the main topics in numerical analysis. The book emphasizes both the theorems which show the underlying rigorous mathematics and the algorithms which define precisely how to program the numerical methods. Both theoretical and practical examples are included.* a unique blend of theory and applications* two brand new chapters on eigenvalues and splines* inclusion of formal algorithms* numerous fully worked examples* a large number of problems, many with solutions

Note: Description based upon print version of record. , Front Cover; Theory and Applications of Numerical Analysis; Copyright Page; Contents; Preface; From the preface to the first edition; Chapter 1. Introduction; 1.1 What is numerical analysis?; 1.2 Numerical algorithms; 1.3 Properly posed and well-conditioned problems; Problems; Chapter 2. Basic analysis; 2.1 Functions; 2.2 Limits and derivatives; 2.3 Sequences and series; 2.4 Integration; 2.5 Logarithmic and exponential functions; Problems; Chapter 3. Taylor's polynomial and series; 3.1 Function approximation; 3.2 Taylor's theorem; 3.3 Convergence of Taylor series , 3.4 Taylor series in two variables3.5 Power series; Problems; Chapter 4. The interpolating polynomial; 4.1 Linear interpolation; 4.2 Polynomial interpolation; 4.3 Accuracy of interpolation; 4.4 The Neville-Aitken algorithm; 4.5 Inverse interpolation; 4.6 Divided differences; 4.7 Equally spaced points; 4.8 Derivatives and differences; 4.9 Effect of rounding error; 4.10 Choice of interpolating points; 4.11 Examples of Bemstein and Runge; Problems; Chapter 5. 'Best' approximation; 5.1 Norms of functions; 5.2 Best approximations; 5.3 Least squares approximation; 5.4 Orthogonal functions , 5.5 Orthogonal polynomials5.6 Minimax approximation; 5.7 Chebyshev series; 5.8 Economization of power series; 5.9 The Remez algorithms; 5.10 Further results on minimax approximation; Problems; Chapter 6. Splines and other approximations; 6.1 Introduction; 6.2 B-splines; 6.3 Equally spaced knots; 6.4 Hermite interpolation; 6.5 Padé and rational approximation; Problems; Chapter 7. Numerical integration and differentiation; 7.1 Numerical integration; 7.2 Romberg integration; 7.3 Gaussian integration; 7.4 Indefinite integrals; 7.5 Improper integrals; 7.6 Multiple integrals , 7.7 Numerical differentiation7.8 Effect of errors; Problems; Chapter 8. Solution of algebraic equations of one variable; 8.1 Introduction; 8.2 The bisection method; 8.3 Interpolation methods; 8.4 One-point iterative methods; 8.5 Faster convergence; 8.6 Higher order processes; 8.7 The contraction mapping theorem; Problems; Chapter 9. Linear equations; 9.1 Introduction; 9.2 Matrices; 9.3 Linear equations; 9.4 Pivoting; 9.5 Analysis of elimination method; 9.6 Matrix factorization; 9.7 Compact elimination methods; 9.8 Symmetric matrices; 9.9 Tridiagonal matrices , 9.10 Rounding errors in solving linear equations Problems; Chapter 10. Matrix norms and applications; 10.1 Determinants, eigenvalues and eigenvectors; 10.2 Vector norms; 10.3 Matrix norms; 10.4 Conditioning; 10.5 Iterative correction from residual vectors; 10.6 Iterative methods; Problems; Chapter 11. Matrix eigenvalues and eigenvectors; 11.1 Relations between matrix norms and eigenvalues; Gerschgorin theorems; 11.2 Simple and inverse iterative method; 11.3 Sturm sequence method; 11.4 The QR algorithm; 11.5 Reduction to tridiagonal form: Householder's method; Problems , Chapter 12. Systems of non-linear equations , English

Additional Edition: ISBN 0-12-553560-0

Language: English