Monte Carlo Strategies in Scientific Computing By Jun S. Liu
Monte Carlo Strategies in Scientific Computing By Jun S. Liu
Publisher: Springer | 2002-10-17 | ISBN: 0387952306 | Pages: 360 | PDF | 3.26 MB
Publisher: Springer | 2002-10-17 | ISBN: 0387952306 | Pages: 360 | PDF | 3.26 MB
A large number of scientists and engineers employ Monte Carlo simulation and related global optimization techniques (such as simulated annealing) as an essential tool in their work. For such scientists, there is a need to keep up to date with several recent advances in Monte Carlo methodologies such as cluster methods, data- augmentation, simulated tempering and other auxiliary variable methods. There is also a trend in moving towards a population-based approach. All these advances in one way or another were motivated by the need to sample from very complex distribution for which traditional methods would tend to be trapped in local energy minima. It is our aim to provide a self-contained and up to date treatment of the Monte Carlo method to this audience.