Stochastic Simulation.

Format:

Book

Author/Creator:

Ripley, Brian D.

Series:

Wiley Series in Probability and Statistics

Wiley Series in Probability and Statistics ; v.316

Language:

English

Subjects (All):

Computer graphics.

Digital computer simulation.

Mathematical models.

Stochastic processes.

Local Subjects:

Computer graphics.

Digital computer simulation.

Mathematical models.

Stochastic processes.

Physical Description:

1 online resource (258 p.)

Edition:

1st edition

Other Title:

Wiley Series in Probability and Statistics Ser.

Wiley Series in Probability and Statistics

Wiley series in probability and mathematical statistics. Applied probability and statistics,

Place of Publication:

New York : Wiley, 2009.

Language Note:

English

System Details:

text file

Summary:

WILEY-INTERSCIENCE PAPERBACK SERIESThe Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists."". . .this is a very competently written and useful addition to the statistical literature; a book every statistician should look at and that many should study!""-Short Book Re

Contents:

Stochastic Simulation; Contents; 1. Aims of Simulation; 1.1. The Tools; 1.2. Models; 1.3. Simulation as Experimentation; 1.4. Simulation in Inference; 1.5. Examples; 1.6. Literature; 1.7. Convention; Exercises; 2. Pseudo-Random Numbers; 2.1. History and Philosophy; 2.2. Congruential Generators; 2.3. Shift-Register Generators; 2.4. Lattice Structure; 2.5. Shuffing and Testing; 2.6. Conclusions; 2.7. Proofs; Exercises; 3. Random Variables; 3.1. Simple Examples; 3.2. General Principles; 3.3. Discrete Distributions; 3.4. Continuous Distributions; 3.5. Recommendations; Exercises

4. Stochastic Models4.1. Order Statistics; 4.2. Multivariate Distributions; 4.3. Poisson Processes and Lifetimes; 4.4. Markov Processes; 4.5. Gaussian Processes; 4.6. Point Processes; 4.7. Metropolis' Method and Random Fields; Exercises; 5. Variance Reduction; 5.1. Monte-Carlo Integration; 5.2. Importance Sampling; 5.3. Control and Antithetic Variates; 5.4. Conditioning; 5.5. Experimental Design; Exercises; 6. Output Analysis; 6.1. The Initial Transient; 6.2. Batching; 6.3. Time-Series Methods; 6.4. Regenerative Simulation; 6.5. A Case Study; Exercises; 7. Uses of Simulation

7.1. Statistical Inference7.2. Stochastic Methods in Optimization; 7.3. Systems of Linear Equations; 7.4. Quasi-Monte-Carlo Integration; 7.5. Sharpening Buffon's Needle; Exercises; References; Appendix A. Computer Systems; Appendix B. Computer Programs; B.1. Form a x b mod c; B.2. Check Primitive Roots; B.3. Lattice Constants for Congruential Generators; B.4. Test GFSR Generators; B.5. Normal Variates; B.6. Exponential Variates; B.7. Gamma Variates; B.8. Discrete Distributions; Index

Notes:

Description based upon print version of record.

Includes bibliographical references and index.

OCLC:

264621199