Format:
1 Online-Ressource (IX, 194 Seiten)
ISBN:
9789811927157
Series Statement:
Springer eBook Collection
Content:
This textbook explains the fundamentals of Markov Chain Monte Carlo (MCMC) without assuming advanced knowledge of mathematics and programming. MCMC is a powerful technique that can be used to integrate complicated functions or to handle complicated probability distributions. MCMC is frequently used in diverse fields where statistical methods are important – e.g. Bayesian statistics, quantum physics, machine learning, computer science, computational biology, and mathematical economics. This book aims to equip readers with a sound understanding of MCMC and enable them to write simulation codes by themselves. The content consists of six chapters. Following Chapter 2, which introduces readers to the Monte Carlo algorithm and highlights the advantages of MCMC, Chapter 3 presents the general aspects of MCMC. Chapter 4 illustrates the essence of MCMC through the simple example of the Metropolis algorithm. In turn, Chapter 5 explains the HMC algorithm, Gibbs sampling algorithm and Metropolis-Hastings algorithm, discussing their pros, cons and pitfalls. Lastly, Chapter 6 presents several applications of MCMC. Including a wealth of examples and exercises with solutions, as well as sample codes and further math topics in the Appendix, this book offers a valuable asset for students and beginners in various fields.
Note:
Chapter 1: Introduction -- Chapter 2: What is the Monte Carlo method? -- Chapter 3: General Aspects of Markov Chain Monte Carlo -- Chapter 4: Metropolis Algorithm -- Chapter 5: Other Useful Algorithms -- Chapter 6: Applications of Markov Chain Monte Carlo.
Additional Edition:
ISBN 9789811927140
Additional Edition:
ISBN 9789811927164
Additional Edition:
ISBN 9789811927171
Additional Edition:
Erscheint auch als Druck-Ausgabe Hanada, Masanori MCMC from scratch Singapore : Springer Nature, 2022 ISBN 9789811927140
Language:
English
Keywords:
Markov-Ketten-Monte-Carlo-Verfahren
DOI:
10.1007/978-981-19-2715-7
