**Non-Uniform Random Variate Generation**

by Luc Devroye

**Publisher**: Springer 1986**ISBN/ASIN**: 0387963057**ISBN-13**: 9780387963051**Number of pages**: 843

**Description**:

This text is about one small field on the crossroads of statistics, operations research and computer science. Statisticians need random number generators to test and compare estimators before using them in real life. In operations research, random numbers are a key component in large scale simulations. Computer scientists need randomness in program testing, game playing and comparisons of algorithms.

Download or read it online for free here:

**Download link**

(37MB, ZIP/PDF)

Download mirrors:**Mirror 1**

## Similar books

**Theory of Probability: A Historical Essay**

by

**Oscar Sheynin**-

**arXiv.org**

This book covers the history of probability up to Kolmogorov with essential additional coverage of statistics up to Fisher. The book covers an extremely wide field, and is targeted at the same readers as any other book on history of science.

(

**2089**views)

**Lectures on Noise Sensitivity and Percolation**

by

**Christophe Garban, Jeffrey E. Steif**-

**arXiv**

The goal of this set of lectures is to combine two seemingly unrelated topics: (1) The study of Boolean functions, a field particularly active in computer science; (2) Some models in statistical physics, mostly percolation.

(

**7225**views)

**Probability and Statistics for Geophysical Processes**

by

**D. Koutsoyiannis**-

**National Technical University of Athens**

Contents: The utility of probability; Basic concepts of probability; Elementary statistical concepts; Special concepts of probability theory in geophysical applications; Typical univariate statistical analysis in geophysical processes; etc.

(

**2116**views)

**Lectures on Stochastic Analysis**

by

**Thomas G. Kurtz**-

**University of Wisconsin**

Covered topics: stochastic integrals with respect to general semimartingales, stochastic differential equations based on these integrals, integration with respect to Poisson measures, stochastic differential equations for general Markov processes.

(

**9504**views)