Monte Carlo Strategies in Scientific Computing

Monte Carlo Strategies in Scientific Computing by Jun S. Liu.

This paperback edition is a reprint of the 2001 Springer edition. This book provides a self-contained and up-to-date treatment of the Monte Carlo method and develops a common framework under which various Monte Carlo techniques can be "standardized" and compared. Given the interdisciplinar...

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Bibliographic Details
Main Author: Liu, Jun S. (Author)
Corporate Author: SpringerLink (Online service)
Format: eBook
Language:English
Published: New York, NY : Springer New York : Imprint: Springer, 2004.
Edition:1st ed. 2004.
Series:Springer Series in Statistics,
Springer eBook Collection.
Subjects:
Statistics .
Computer mathematics.
Probabilities.
Physics.
Electronic resources (E-books)
Online Access:Click to view e-book
Holy Cross Note:Loaded electronically.
Electronic access restricted to members of the Holy Cross Community.
Table of Contents:
  • 1 Introduction and Examples
  • 2 Basic Principles: Rejection, Weighting, and Others
  • 3 Theory of Sequential Monte Carlo
  • 4 Sequential Monte Carlo in Action
  • 5 Metropolis Algorithm and Beyond
  • 6 The Gibbs Sampler
  • 7 Cluster Algorithms for the Ising Model
  • 8 General Conditional Sampling
  • 9 Molecular Dynamics and Hybrid Monte Carlo
  • 10 Multilevel Sampling and Optimization Methods
  • 11 Population-Based Monte Carlo Methods
  • 12 Markov Chains and Their Convergence
  • 13 Selected Theoretical Topics
  • A Basics in Probability and Statistics
  • A.1 Basic Probability Theory
  • A.1.1 Experiments, events, and probability
  • A.1.2 Univariate random variables and their properties
  • A.1.3 Multivariate random variable
  • A.1.4 Convergence of random variables
  • A.2 Statistical Modeling and Inference
  • A.2.1 Parametric statistical modeling
  • A.2.2 Frequentist approach to statistical inference
  • A.2.3 Bayesian methodology
  • A.3 Bayes Procedure and Missing Data Formalism
  • A.3.1 The joint and posterior distributions
  • A.3.2 The missing data problem
  • A.4 The Expectation-Maximization Algorithm
  • References
  • Author Index.

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