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MCMC from Scratch

- A Practical Introduction to Markov Chain Monte Carlo
Af: Masanori Hanada, So Matsuura Engelsk Hardback

MCMC from Scratch

- A Practical Introduction to Markov Chain Monte Carlo
Af: Masanori Hanada, So Matsuura Engelsk Hardback
Tjek vores konkurrenters priser

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. 


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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. 


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Se mere i:
Produktdetaljer
Sprog: Engelsk
Sider: 194
ISBN-13: 9789811927140
Indbinding: Hardback
Udgave:
ISBN-10: 9811927146
Kategori: Biofysik
Udg. Dato: 21 okt 2022
Længde: 18mm
Bredde: 241mm
Højde: 159mm
Forlag: Springer Verlag, Singapore
Oplagsdato: 21 okt 2022
Forfatter(e): Masanori Hanada, So Matsuura
Forfatter(e) Masanori Hanada, So Matsuura

Kategori Biofysik

ISBN-13 9789811927140

Sprog Engelsk

Indbinding Hardback

Sider 194

Udgave

Længde 18mm

Bredde 241mm

Højde 159mm

Udg. Dato 21 okt 2022

Oplagsdato 21 okt 2022

Forlag Springer Verlag, Singapore

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