TY  - BOOK
AU  - Hanada, Masanori
AU  - Matsuura, So
TI  - MCMC from Scratch: a Practical Introduction to Markov Chain Monte Carlo; 1st Edition
CY  - Singapore
PB  - Springer
M1  - PUBDB-2025-05175
SN  - 9789811927171
T2  - Springer eBook Collection
SP  - 1 Online-Ressource (IX, 194 Seiten
PY  - 2022
AB  - 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
LB  - PUB:(DE-HGF)3
DO  - DOI:10.1007/978-981-19-2715-7
UR  - https://bib-pubdb1.desy.de/record/641764
ER  -