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Monte Carlo Methods by Adrian Barbu (English) Hardcover Book
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eBay-objectnummer:386986545951
Specificaties
- Objectstaat
- ISBN-13
- 9789811329708
- Book Title
- Monte Carlo Methods
- ISBN
- 9789811329708
- Subject Area
- Computers, Mathematics
- Publication Name
- Monte Carlo Methods
- Publisher
- Springer
- Item Length
- 9.4 in
- Subject
- Numerical Analysis, Probability & Statistics / General, Computer Science
- Publication Year
- 2020
- Type
- Textbook
- Format
- Hardcover
- Language
- English
- Item Weight
- 36.5 Oz
- Item Width
- 6.6 in
- Number of Pages
- Xvi, 422 Pages
Over dit product
Product Information
This book seeks to bridge the gap between statistics and computer science. It provides an overview of Monte Carlo methods, including Sequential Monte Carlo, Markov Chain Monte Carlo, Metropolis-Hastings, Gibbs Sampler, Cluster Sampling, Data Driven MCMC, Stochastic Gradient descent, Langevin Monte Carlo, Hamiltonian Monte Carlo, and energy landscape mapping. Due to its comprehensive nature, the book is suitable for developing and teaching graduate courses on Monte Carlo methods. To facilitate learning, each chapter includes several representative application examples from various fields. The book pursues two main goals: (1) It introduces researchers to applying Monte Carlo methods to broader problems in areas such as Computer Vision, Computer Graphics, Machine Learning, Robotics, Artificial Intelligence, etc.; and (2) it makes it easier for scientists and engineers working in these areas to employ Monte Carlo methods to enhance their research.
Product Identifiers
Publisher
Springer
ISBN-10
9811329702
ISBN-13
9789811329708
eBay Product ID (ePID)
27038703630
Product Key Features
Number of Pages
Xvi, 422 Pages
Language
English
Publication Name
Monte Carlo Methods
Publication Year
2020
Subject
Numerical Analysis, Probability & Statistics / General, Computer Science
Type
Textbook
Subject Area
Computers, Mathematics
Format
Hardcover
Dimensions
Item Weight
36.5 Oz
Item Length
9.4 in
Item Width
6.6 in
Additional Product Features
Reviews
"True to its goal, the text offers a comprehensive overview on Monte Carlo methods. ... this text is a quality reference for researchers interested in computer vision, computer graphics, machine learning, artificial intelligence and related fields." (Grant Innerst, MAA Reviews, July 18, 2021), "This monograph ... is intended to be a textbook for graduate students in statistics, computer science and engineering. It covers a very broad range of topics ... . Each chapter is finished by a rather long list of relevant references. Thus, it can be used also as a reference book by researches in the fields of machine learning, pattern recognition ... . it can be a useful reference to many important Monte Carol methods." (Jaromír Antoch, zbMATH 1483.65001, 2022) "True to its goal, the text offers a comprehensive overview on Monte Carlo methods. ... this text is a quality reference for researchers interested in computer vision, computer graphics, machine learning, artificial intelligence and related fields." (Grant Innerst, MAA Reviews, July18, 2021), "This monograph ... is intended to be a textbook for graduate students in statistics, computer science and engineering. It covers a very broad range of topics ... . Each chapter is finished by a rather long list of relevant references. Thus, it can be used also as a reference book by researches in the fields of machine learning, pattern recognition ... . it can be a useful reference to many important Monte Carol methods." (Jaromír Antoch, zbMATH 1483.65001, 2022) "True to its goal, the text offers a comprehensive overview on Monte Carlo methods. ... this text is a quality reference for researchers interested in computer vision, computer graphics, machine learning, artificial intelligence and related fields." (Grant Innerst, MAA Reviews, July 18, 2021)
Number of Volumes
1 Vol.
Illustrated
Yes
Lc Classification Number
Qa71-90
Table of Content
1 Introduction to Monte Carlo Methods.- 2 Sequential Monte Carlo.- 3 Markov Chain Monte Carlo - the Basics.- 4 Metropolis Methods and Variants.- 5 Gibbs Sampler and its Variants.- 6 Cluster Sampling Methods.- 7 Convergence Analysis of MCMC.- 8 Data Driven Markov Chain Monte Carlo.- 9 Hamiltonian and Langevin Monte Carlo.- 10 Learning with Stochastic Gradient.- 11 Mapping the Energy Landscape.
Copyright Date
2020
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