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MCMC from Scratch: A Practical Introduction to Markov Chain Monte Carlo
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MCMC from Scratch: A Practical Introduction to Markov Chain Monte Carlo

MCMC from Scratch: A Practical Introduction to Markov Chain Monte Carlo

Masanori / Matsuura Hanada

194 pages, parution le 20/10/2022

Résumé

This textbook explains the fundamentals of Markov Chain Monte Carlo (MCMC) without assuming advanced knowledge of mathematics and programming. 2, which introduces readers to the Monte Carlo algorithm and highlights the advantages of MCMC, Chap.

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. 5 explains 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.


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. Masanori Hanada is a theoretical physicist at the Department of Mathematics, the University of Surrey. 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.

Caractéristiques techniques

  PAPIER
Éditeur(s) Apress
Auteur(s) Masanori / Matsuura Hanada
Parution 20/10/2022
Nb. de pages 194
EAN13 9789811927140

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