eBook ISBN:  9781470431433 
Product Code:  FIM/16.E 
List Price:  $40.00 
MAA Member Price:  $36.00 
AMS Member Price:  $32.00 
eBook ISBN:  9781470431433 
Product Code:  FIM/16.E 
List Price:  $40.00 
MAA Member Price:  $36.00 
AMS Member Price:  $32.00 

Book DetailsFields Institute MonographsVolume: 16; 2002; 103 ppMSC: Primary 65; 60; Secondary 82;
Monte Carlo methods form an experimental branch of mathematics that employs simulations driven by random number generators. These methods are often used when others fail, since they are much less sensitive to the “curse of dimensionality”, which plagues deterministic methods in problems with a large number of variables. Monte Carlo methods are used in many fields: mathematics, statistics, physics, chemistry, finance, computer science, and biology, for instance.
This book is an introduction to Monte Carlo methods for anyone who would like to use these methods to study various kinds of mathematical models that arise in diverse areas of application. The book is based on lectures in a graduate course given by the author. It examines theoretical properties of Monte Carlo methods as well as practical issues concerning their computer implementation and statistical analysis. The only formal prerequisite is an undergraduate course in probability.
The book is intended to be accessible to students from a wide range of scientific backgrounds. Rather than being a detailed treatise, it covers the key topics of Monte Carlo methods to the depth necessary for a researcher to design, implement, and analyze a full Monte Carlo study of a mathematical or scientific problem. The ideas are illustrated with diverse running examples. There are exercises sprinkled throughout the text. The topics covered include computer generation of random variables, techniques and examples for variance reduction of Monte Carlo estimates, Markov chain Monte Carlo, and statistical analysis of Monte Carlo output.ReadershipAdvanced undergraduates, graduate students, research mathematicians, statisticians, physicists, chemists, engineers, and computer scientists interested in numerical analysis, probability theory, and stochastic processes.

Table of Contents

Chapters

Chapter 1. Introduction

Chapter 2. Generating random numbers

Chapter 3. Variance reduction techniques

Chapter 4. Markov chain Monte Carlo

Chapter 5. Statistical analysis of simulation output

Chapter 6. The Ising model and related examples


Reviews

Short but enlightening introduction to the subject … ideal text for a first graduate course on Monte Carlo methods for statisticians, mathematicians, computer scientists, and other scientists. The author is a leading expert in the field and he has made a careful choice of material to give readers a good basic introduction to the field.
CMS Notes


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Monte Carlo methods form an experimental branch of mathematics that employs simulations driven by random number generators. These methods are often used when others fail, since they are much less sensitive to the “curse of dimensionality”, which plagues deterministic methods in problems with a large number of variables. Monte Carlo methods are used in many fields: mathematics, statistics, physics, chemistry, finance, computer science, and biology, for instance.
This book is an introduction to Monte Carlo methods for anyone who would like to use these methods to study various kinds of mathematical models that arise in diverse areas of application. The book is based on lectures in a graduate course given by the author. It examines theoretical properties of Monte Carlo methods as well as practical issues concerning their computer implementation and statistical analysis. The only formal prerequisite is an undergraduate course in probability.
The book is intended to be accessible to students from a wide range of scientific backgrounds. Rather than being a detailed treatise, it covers the key topics of Monte Carlo methods to the depth necessary for a researcher to design, implement, and analyze a full Monte Carlo study of a mathematical or scientific problem. The ideas are illustrated with diverse running examples. There are exercises sprinkled throughout the text. The topics covered include computer generation of random variables, techniques and examples for variance reduction of Monte Carlo estimates, Markov chain Monte Carlo, and statistical analysis of Monte Carlo output.
Advanced undergraduates, graduate students, research mathematicians, statisticians, physicists, chemists, engineers, and computer scientists interested in numerical analysis, probability theory, and stochastic processes.

Chapters

Chapter 1. Introduction

Chapter 2. Generating random numbers

Chapter 3. Variance reduction techniques

Chapter 4. Markov chain Monte Carlo

Chapter 5. Statistical analysis of simulation output

Chapter 6. The Ising model and related examples

Short but enlightening introduction to the subject … ideal text for a first graduate course on Monte Carlo methods for statisticians, mathematicians, computer scientists, and other scientists. The author is a leading expert in the field and he has made a careful choice of material to give readers a good basic introduction to the field.
CMS Notes