Preface 1 Introduction 1.1 What is a Monte Carlo simulation? 1.2 What porblems can we solve with it? 1.3 What difficulties will we encounter? 1.4 What strategr Should we follow in approaching a problem? 1.5 How do simulations relate to theory and experiment? 2 Some necessary background 2.1 Thermodynamics and statistical mechanics:a quick reminder 2.2 Probability theory 2.3 Non-equilirium and dynamics:some introductory comments 3 Simple sampling Monte Carol methods 3.1 Introduction 3.2 Comparisons of methods for numerical integration of given functions 3.3 Boundary value problems 3.4 Simulation of radionactive decay 3.5 Simulation of transport properties 3.6 The Percolation Problem 3.7 Finding the guoundstate of a Hamiltonian 3.8 Generation of ‘random’walks 3.9 Final remarks References 4 Importance sampling Monte Carlo methods 4.1 Introduction 4.2 The simplest case:single spin-flip sampling for the simple Ising model 4.3 Other discrete variable models 4.4 Spin-exchange sampling 4.5 Microcanonical methods 4.6 General remarks,choice of ensemble 4.7 Statics and dynamics of polymer models on lattices 4.8 Some advice References 5 More on importance sampling Carlo methods for lattice systms 6 Off-lattice models 7 Reweighting methods 8 Quantum Monte Carlo methods 9 Moten Carlo renormalization group methods 10 Non-equilibrium and irreversible processes 11 Lattice gauge models:brief introduction 12 A brief revieew of other methods of computer simulation 13 Outlook Appendix:listing of programs mentioned in the text Index
