统计物理学中的蒙特卡洛模拟主要处理凝聚态物理学的多体系统和相关物理学、化学及其他方面的计算模拟，甚至渗透到交通流、股票市场波动等等领域。书中描述了多变量蒙特卡洛模拟方法的理论背景，给出了初学者学习进行模拟和结果分析的系统演示。《统计物理学中的蒙特卡罗模拟(第5版)》由(德)宾德著，不仅包括经典方法，也包括蒙特卡洛模拟方法；增加了一章专门讲述自由能景观采样。目次：导论：该书的遵旨和内容范围；蒙特卡洛方法的理论基础及其在统计物理学中的应用；蒙特卡洛方法实际应用指导；蒙特卡洛方法论的最近重要进展；量子蒙特卡洛模拟入门；自由能景观样本的蒙特卡洛。

1 Introduction: Purpose and Scope of This Volume, and Some

General Comments

2 Theoretical Foundations of the Monte Carlo Method

and Its Applications in Statistical Physics

 2.1 Simple Sampling Versus Importance Sampling

 2.1.1 Models

 2.1.2 Simple Sampling

 2.1.3 Random Walks and Self-Avoiding Walks

 2.1.4 Thermal Averages by the Simple Sampling Method

 2.1.5 Advantages and Limitations of Simple Sampling

 2.1.6 Importance Sampling

 2.1.7 More About Models and Algorithms

 2.2 Organization of Monte Carlo Programs, and the Dynamic

Interpretation of Monte Carlo Sampling

 2.2.1 First Comments on the Simulation of the Ising Model

 2.2.2 Boundary Conditions

 2.2.3 The Dynamic Interpretation of the Importance

Sampling Monte Carlo Method

 2.2.4 Statistical Errors and Time-Displaced Relaxation Functions

 2.3 Finite-Size Effects

 2.3.1 Finite-Size Effects at the Percolation Transition

 2.3.2 Finite-Size Scaling for the Percolation Problem

 2.3.3 Broken Symmetry and Finite-Size Effects

at Thermal Phase Transitions

 2.3.4 The Order Parameter Probability Distribution

and Its Use to Justify Finite-Size Scaling

and Phenomenological Renormalization

 2.3.5 Finite-Size Behavior of Relaxation Times

 2.3.6 Finite-Size Scaling Without "Hyperscaling"

 2.3.7 Finite-Size Scaling for First-Order Phase Transitions

 2.3.8 Finite-Size Behavior of Statistical Errors

and the Problem of Self-Averaging

 2.4 Remarks on the Scope of the Theory Chapter

3 Guide to Practical Work with the Monte Carlo Method

 3.1 Aims of the Guide

 3.2 Simple Sampling

 3.2.1 RandomWalk

 3.2.2 Nonreversal Random Walk

 3.2.3 Self-Avoiding Random Walk

 3.2.4 Percolation

 3.3 Biased Sampling

 3.3.1 Self-Avoiding Random Walk

 3.4 Importance Sampling

 3.4.1 Ising Model

 3.4.2 Serf-Avoiding Random Walk

4 Some Important Recent Developments of the Monte Carlo

Methodology

 4.1 Introduction

 4.2 Application of the Swendsen-Wang Cluster Algorithm

to the Ising Model

 4.3 Reweighting Methods in the Study of Phase Diagrams,

First-Order Phase Transitions, and Interfacial Tensions

 4.4 Some Comments on Advances with Finite-Size Scaling Analyses

5 Quantum Monte Carlo Simulations: An Introduction

 5.1 Quantum Statistical Mechanics Versus Classical

Statistical Mechanics

 5.2 The Path Integral Quantum Monte Carlo Method

 5.3 Quantum Monte Carlo for Lattice Models

 5.4 Concluding Remarks

6 Monte Carlo Methods for the Sampling of Free Energy Landscapes

 6.1 Introduction and Overview

 6.2 Umbrella Sampling

 6.3 Multicanonical Sampling and Other "Extended

Ensemble" Methods

 6.4 Wang-Landau Sampling

 6.5 Transition Path Sampling

 6.6 Concluding Remarks

Appendix

 A.1 Algorithm for the Random Walk Problem

 A.2 Algorithm for Cluster Identification

References

Bibliography

Subject Index