ABSTRACT
The Monte Carlo method is a statistical technique that is capable of simulating a mathematical or physical experiment on a computer. In mathematics, it can provide the expectation value of functions and evaluate integrals; in science and engineering, it is capable of simulating of complex problems that are comprised of various random processes with known or assumed probability density functions. To be able to simulate the random process, i.e., sample from a probability function for an event, it uses random numbers or pseudorandom numbers. Just like any statistical process, the Monte Carlo method requires repetition to achieve a small relative uncertainty, and, therefore, may necessitate impractically large simulation times. To overcome this difculty, parallel algorithms and variance reduction techniques are needed. This book attempts to address major topics affecting development, utilization, and performance of a Monte Carlo algorithm.
