Mcmc from Scratch: A Practical Introduction to Markov Chain Monte Carlo - Brossura

Hanada, Masanori; Matsuura, So

Brossura

ISBN 10: 9811927170 ISBN 13: 9789811927171

Casa editrice: Springer-Nature New York Inc, 2023

Riassunto

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. 

Su questo libro

Informazioni sull?autore

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.

Dalla quarta di copertina

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.

Dettagli dell?edizione

• Editore: Springer-Nature New York Inc
• Data di pubblicazione: 2023
• Lingua: Inglese
• ISBN 10: 9811927170
• ISBN 13: 9789811927171
• Rilegatura: Copertina flessibile
• Numero edizione: 1
• Numero di pagine: 204
• Contatto del produttore: Springer Nature Customer Service Center GmbH; ProductSafety@springernature.com