Non-Uniform Random Variate Generation

This book evolves around the expected complexity of random variate generation algorithms. It sets up an idealized computational model, introduces the notion of uniformly bounded expected complexity, and studies bounds for computational complexity.

**Tag(s):**
Operations Research

**Publication date**: 31 Dec 1986

**ISBN-10**:
n/a

**ISBN-13**:
n/a

**Paperback**:
857 pages

**Views**: 14,173

Non-Uniform Random Variate Generation

This book evolves around the expected complexity of random variate generation algorithms. It sets up an idealized computational model, introduces the notion of uniformly bounded expected complexity, and studies bounds for computational complexity.

Terms and Conditions:

Book Excerpts:

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.

The applications are wide and varied. Yet all depend upon the same computer generated random numbers. Usually, the randomness demanded by an application has some built-in structure: typically, one needs more than just a sequence of independent random bits or independent uniform [0,1] random variables. Some users need random variables with unusual densities, or random combinatorial objects with specific properties, or random geometric objects, or random processes with well defined dependence structures. This is precisely the subject area of the book, the study of non-uniform random variates.

The plot evolves around the expected complexity of random variate generation algorithms. This book sets up an idealized computational model (without overdoing it), introduces the notion of uniformly bounded expected complexity, and studies upper and lower bounds for computational complexity. In short, a touch of computer science is added to the field. To keep everything abstract, no timings or computer programs are included.

Luc Devroye wrote:As the book is out of print, the copyright and ownership is mine, so I do with it what I want. On these web pages, you will find a fine scan of my book in text searchable PDF format (thanks, HK). This is the original text. A list of errata is here.

Furthermore, I give anyone the permission, even without asking me, to take these PDF files to a printer, print as many copies as you like, and sell them for profit. If you would like me to advertise the sales points of the hard copies, please let me know. To the libraries: Please do not charge patrons for copying this book. I grant everyone the right to copy at will, for free.

Book Excerpts:

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.

The applications are wide and varied. Yet all depend upon the same computer generated random numbers. Usually, the randomness demanded by an application has some built-in structure: typically, one needs more than just a sequence of independent random bits or independent uniform [0,1] random variables. Some users need random variables with unusual densities, or random combinatorial objects with specific properties, or random geometric objects, or random processes with well defined dependence structures. This is precisely the subject area of the book, the study of non-uniform random variates.

The plot evolves around the expected complexity of random variate generation algorithms. This book sets up an idealized computational model (without overdoing it), introduces the notion of uniformly bounded expected complexity, and studies upper and lower bounds for computational complexity. In short, a touch of computer science is added to the field. To keep everything abstract, no timings or computer programs are included.

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