國立虎尾科技大學 |

MCMC from Scratch = A Practical Introduction to Markov Chain Monte Carlo /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	MCMC from Scratch/ by Masanori Hanada, So Matsuura.
其他題名:	A Practical Introduction to Markov Chain Monte Carlo /
作者:	Hanada, Masanori.
其他作者:	Matsuura, So.
面頁冊數:	IX, 194 p. 69 illus., 24 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Particle Physics. -
電子資源:	https://doi.org/10.1007/978-981-19-2715-7
ISBN:	9789811927157

MCMC from Scratch = A Practical Introduction to Markov Chain Monte Carlo /
Hanada, Masanori.

MCMC from ScratchA Practical Introduction to Markov Chain Monte Carlo /[electronic resource] :by Masanori Hanada, So Matsuura. - 1st ed. 2022. - IX, 194 p. 69 illus., 24 illus. in color.online resource.

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.

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.

ISBN: 9789811927157

Standard No.: 10.1007/978-981-19-2715-7doiSubjects--Topical Terms:

1366292
Particle Physics.

LC Class. No.: QA76.9.A43

Dewey Class. No.: 518.1

MCMC from Scratch = A Practical Introduction to Markov Chain Monte Carlo /
LDR:02973nam a22003975i 4500 001 1084575
003 DE-He213
005 20221020112625.0
007 cr nn 008mamaa
008 221228s2022 si | s |||| 0|eng d
020 $a 9789811927157 $9 978-981-19-2715-7
024 7 $a 10.1007/978-981-19-2715-7 $2 doi
035 $a 978-981-19-2715-7
050 4 $a QA76.9.A43
072 7 $a UMB $2 bicssc
072 7 $a COM051300 $2 bisacsh
072 7 $a UMB $2 thema
082 0 4 $a 518.1 $2 23
100 1 $a Hanada, Masanori. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1390921
245 1 0 $a MCMC from Scratch $h [electronic resource] : $b A Practical Introduction to Markov Chain Monte Carlo / $c by Masanori Hanada, So Matsuura.
250 $a 1st ed. 2022.
264 1 $a Singapore : $b Springer Nature Singapore : $b Imprint: Springer, $c 2022.
300 $a IX, 194 p. 69 illus., 24 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
505 0 $a 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.
520 $a 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.
650 2 4 $a Particle Physics. $3 1366292
650 2 4 $a Machine Learning. $3 1137723
650 2 4 $a Statistics. $3 556824
650 0 $a Biophysics. $3 581576
650 0 $a Particles (Nuclear physics). $3 1366291
650 0 $a Machine learning. $3 561253
650 0 $a Statistics . $3 1253516
650 0 $a Algorithms. $3 527865
700 1 $a Matsuura, So. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1390922
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9789811927140
776 0 8 $i Printed edition: $z 9789811927164
776 0 8 $i Printed edition: $z 9789811927171
856 4 0 $u https://doi.org/10.1007/978-981-19-2715-7
912 $a ZDB-2-SMA
912 $a ZDB-2-SXMS
950 $a Mathematics and Statistics (SpringerNature-11649)
950 $a Mathematics and Statistics (R0) (SpringerNature-43713)