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

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Item specifics

Condition
Good: A book that has been read but is in good condition. Very minimal damage to the cover including ...
ISBN
9789811927140
Category

About this product

Product Identifiers

Publisher
Springer
ISBN-10
9811927146
ISBN-13
9789811927140
eBay Product ID (ePID)
23057270988

Product Key Features

Number of Pages
IX, 194 Pages
Language
English
Publication Name
Mcmc from Scratch : a Practical Introduction to Markov Chain Monte Carlo
Subject
Programming / Algorithms, Intelligence (Ai) & Semantics, Life Sciences / Biophysics, Probability & Statistics / General, Physics / Nuclear
Publication Year
2022
Type
Textbook
Subject Area
Mathematics, Computers, Science
Author
So Matsuura, Masanori Hanada
Format
Hardcover

Dimensions

Item Weight
18.1 Oz
Item Length
9.3 in
Item Width
6.1 in

Additional Product Features

Dewey Edition
23
Number of Volumes
1 vol.
Illustrated
Yes
Dewey Decimal
519.233
Table Of Content
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.
Synopsis
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.
LC Classification Number
QA76.9.A43

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