Overview
- Explains the fundamentals of MCMC and important algorithms without assuming advanced knowledge of math and programming
- Contains many examples, exercises with solutions, and codes
- Equips readers to write simulation codes by themselves
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About this book
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 Chap. 2, which introduces readers to the Monte Carlo algorithm and highlights the advantages of MCMC, Chap. 3 presents the general aspects of MCMC. Chap. 4 illustrates the essence of MCMC through the simple example of the Metropolis algorithm. In turn, Chap. 5explains the HMC algorithm, Gibbs sampling algorithm and Metropolis-Hastings algorithm, discussing their pros, cons and pitfalls. Lastly, Chap. 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.
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Table of contents (6 chapters)
Authors and Affiliations
About the authors
Masanori Hanada is a theoretical physicist at the School of Mathematical Sciences, Queen Mary University of London. His research interests include strongly coupled quantum systems, quantum field theory, and superstring theory. He and his collaborators pioneered the application of Markov Chain Monte Carlo methods for superstring theory.
So Matsuura is a theoretical physicist at Research and Education Center for Natural Sciences, Keio University. His research interests include superstring theory and nonperturbative lattice formulation of supersymmetry quantum field theory. In addition to physics research, he has a strong passion for public outreach activities and delivers many public lectures.
So Matsuura is a theoretical physicist at Research and Education Center for Natural Sciences, Keio University. His research interests include superstring theory and nonperturbative lattice formulation of supersymmetry quantum field theory. In addition to physics research, he has a strong passion for public outreach activities and delivers many public lectures.
Bibliographic Information
Book Title: MCMC from Scratch
Book Subtitle: A Practical Introduction to Markov Chain Monte Carlo
Authors: Masanori Hanada, So Matsuura
DOI: https://doi.org/10.1007/978-981-19-2715-7
Publisher: Springer Singapore
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022
Hardcover ISBN: 978-981-19-2714-0Published: 21 October 2022
Softcover ISBN: 978-981-19-2717-1Published: 22 October 2023
eBook ISBN: 978-981-19-2715-7Published: 20 October 2022
Edition Number: 1
Number of Pages: IX, 194
Number of Illustrations: 45 b/w illustrations, 24 illustrations in colour
Topics: Algorithms, Statistics, general, Machine Learning, Particle and Nuclear Physics, Biological and Medical Physics, Biophysics