Monte Carlo Methods /

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 C...

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Bibliographic Details
Main Authors:	Barbu, Adrian (Author), Zhu, Song-Chun (Author)
Corporate Author:	SpringerLink (Online service)
Format:	eBook
Language:	English
Published:	Singapore : Springer Singapore : Imprint: Springer, 2020.
Edition:	1st ed. 2020.
Subjects:	Computer mathematics. Mathematical statistics. Optical data processing. Statistics . Computational Mathematics and Numerical Analysis. Probability and Statistics in Computer Science. Computer Imaging, Vision, Pattern Recognition and Graphics. Statistical Theory and Methods. Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. Electronic books.
Online Access:	Connect to the full text of this electronic book

Table of Contents:

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.