**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

**Markov Chains and Mixing Times**

by

**D. A. Levin, Y. Peres, E. L. Wilmer**-

**American Mathematical Society**

An introduction to the modern approach to the theory of Markov chains. The main goal of this approach is to determine the rate of convergence of a Markov chain to the stationary distribution as a function of the size and geometry of the state space.

(

**8912**views)

**A Minimum of Stochastics for Scientists**

by

**Noel Corngold**-

**Caltech**

The book introduces students to the ideas and attitudes that underlie the statistical modeling of physical, chemical, biological systems. The text contains material the author have tried to convey to an audience composed mostly of graduate students.

(

**7522**views)

**Statistics, Probability, and Game Theory: papers in honor of David Blackwell**

by

**David Blackwell, at al.**-

**IMS**

The bulk of the articles in this volume are research articles in probability, statistics, gambling, game theory, Markov decision processes, set theory and logic, comparison of experiments, games of timing, merging of opinions, etc.

(

**8139**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.

(

**9196**views)