This book is devoted to a new branch of experimental design theory called simulation experimental design. There are many books devoted either to the theory of experimental design or to system simulation techniques, but in this book an approach to combine both fields is developed. Especially the mathematical theory of such universal variance reduction techniques as splitting and Russian Roulette is explored. The book contains a number of results on regression design theory related to nonlinear problems, the E-optimum criterion and designs which minimize bias.
Audience: This volume will be of value to readers interested in systems simulation, applied statistics and numerical methods with basic knowledge of applied statistics and linear algebra.

Contenuti:

Preface. 1. Methods of Distribution and Process Simulation. 2. Discrete Markov Chains. 3. Path Branching Technique for Discrete Markov Chains. 4. General Markov Chains. 5. General Design Methods of Simulation and Regression Experiments. References. Index.

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