"Theory and Applications of Monte Carlo Simulations" ed. by Victor (Wai Kin) Chan

The purpose of this book is to introduce researchers and practitioners to recent advances and applications of Monte Carlo Simulation (MCS). Random sampling is the key of the MCS technique. The chapters of this book collectively illustrates how such a sampling technique is exploited to solve difficult problems or analyze complex systems in various engineering and science domains.

Issues related to the use of MCS including goodness-of-fit, uncertainty evaluation, variance reduction, optimization, and statistical estimation are discussed and examples of solutions are given
The aim of this book is to unify knowledge of MCS from different fields to facilitate research and new applications of MCS.

Contents
Preface
1 Monte Carlo Statistical Tests for Identity of Theoretical and Empirical Distributions of Experimental Data
2 Monte Carlo Simulations Applied to Uncertainty in Measurement
3 Fractional Brownian Motions in Financial Models and Their Monte Carlo Simulation
4 Monte-Carlo-Based Robust Procedure for Dynamic Line Layout Problems
5 Comparative Study of Various Self-Consistent Event Biasing Schemes for Monte Carlo Simulations of Nanoscale MOSFETs
6 Atomistic Monte Carlo Simulations on the Formation of Carbonaceous Mesophase in Large Ensembles of Polyaromatic Hydrocarbons
7 Variance Reduction of Monte Carlo Simulation in Nuclear Engineering Field
8 Stochastic Models of Physicochemical Processes in Catalytic Reactions - Self-Oscillations and Chemical Waves in CO Oxidation Reaction
9 Monte-Carlo Simulation of Particle Diffusion in Various Geometries and Application to Chemistry and Biology
10 Kinetic Monte Carlo Simulation in Biophysics and Systems Biology
11 Detection of Breast Cancer Lumps Using Scattered X-Ray Profiles: A Monte Carlo Simulation Study