UID:
almahu_9948025602402882
Umfang:
1 online resource (421 p.)
Ausgabe:
1st edition
ISBN:
1-282-16842-8
,
9786612168420
,
0-08-091936-7
Inhalt:
The Bayesian network is one of the most important architectures for representing and reasoning with multivariate probability distributions. When used in conjunction with specialized informatics, possibilities of real-world applications are achieved. Probabilistic Methods for BioInformatics explains the application of probability and statistics, in particular Bayesian networks, to genetics. This book provides background material on probability, statistics, and genetics, and then moves on to discuss Bayesian networks and applications to bioinformatics. Rather than getting bogged down
Anmerkung:
Description based upon print version of record.
,
Front Cover; Probabilistic Methods for Bioinformatics: with an Introduction to Bayesian Networks; Copyright Page; Contents; Preface; About the Author; Part I: Background; Chapter 1. Probabilistic Informatics; 1.1 What Is Informatics?; 1.2 Bioinformatics; 1.3 Probabilistic Informatics; 1.4 Outline of This Book; Chapter 2. Probability Basics; 2.1 Probability Basics; 2.2 Random Variables; 2.3 The Meaning of Probability; 2.4 Random Variables in Applications; Chapter 3. Statistics Basics; 3.1 Basic Concepts; 3.2 Markov Chain Monte Carlo; 3.3 The Normal Distribution; Chapter 4. Genetics Basics
,
4.1 Organisms and Cells4.2 Genes; 4.3 Mutations; Part II: Bayesian Networks; Chapter 5. Foundations of Bayesian Networks; 5.1 What Is a Bayesian Network?; 5.2 Properties of Bayesian Networks; 5.3 Causal Networks as Bayesian Networks; 5.4 Inference in Bayesian Networks; 5.5 Networks with Continuous Variables; 5.6 How Do We Obtain the Probabilities?; Chapter 6. Further Properties of Bayesian Networks; 6.1 Entailed Conditional Independencies; 6.2 Faithfulness; 6.3 Markov Equivalence; 6.4 Markov Blankets and Boundaries; Chapter 7. Learning Bayesian Network Parameters
,
7.1 Learning a Single Parameter7.2 Learning Parameters in a Bayesian Network; Chapter 8. Learning Bayesian Network Structure; 8.1 Model Selection; 8.2 Score-Based Structure Learning; 8.3 Constraint-Based Structure Learning; 8.4 Causal Learning; 8.5 Model Averaging; 8.6 Approximate Structure Learning; 8.7 Software Packages for Learning; Part III: Bioinformatics Applications; Chapter 9. Nonmolecular Evolutionary Genetics; 9.1 No Mutations, Selection, or Genetic Drift; 9.2 Natural Selection; 9.3 Genetic Drift; 9.4 Natural Selection and Genetic Drift; 9.5 Rate of Substitution
,
Chapter 10. Molecular Evolutionary Genetics10.1 Models of Nucleotide Substitution; 10.2 Evolutionary Distance; 10.3 Sequence Alignment; Chapter 11. Molecular Phylogenetics; 11.1 Phylogenetic Trees; 11.2 Distance Matrix Learning Methods; 11.3 Maximum Likelihood Method; 11.4 Distance Matrix Methods Using ML; Chapter 12. Analyzing Gene Expression Data; 12.1 DNA Microarrays; 12.2 A Bootstrap Approach; 12.3 Model Averaging Approaches; 12.4 Module Network Approach; Chapter 13. Genetic Linkage Analysis; 13.1 Introduction to Genetic Linkage Analysis; 13.2 Genetic Linkage Analysis in Humans
,
13.3 A Bayesian Network ModelBibliography; Index
,
English
Weitere Ausg.:
ISBN 0-12-370476-6
Sprache:
Englisch
Bookmarklink
