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Algorithmic Probability (Hardcover)

Marcel F. Neuts

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ISBN 10: 041299691X / ISBN 13: 9780412996917
Nuevos Condición: New Encuadernación de tapa dura
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Descripción

Hardcover. This unique text collects more than 400 problems in combinatorics, derived distributions, discrete and continuous Markov chains, and models requiring a computer experimental a.Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. 472 pages. 0.839. N° de ref. de la librería 9780412996917

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Detalles bibliográficos

Título: Algorithmic Probability (Hardcover)

Año de publicación: 1995

Encuadernación: Hardcover

Condición del libro:New

Edición: 1st.

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Sinopsis:

This unique text collects more than 400 problems in combinatorics, derived distributions, discrete and continuous Markov chains, and models requiring a computer experimental approach. The first book to deal with simplified versions of models encountered in the contemporary statistical or engineering literature, Algorithmic Probability emphasizes correct interpretation of numerical results and visualization of the dynamics of stochastic processes.

A significant contribution to the field of applied probability, Algorithmic Probability is ideal both as a secondary text in probability courses and as a reference. Engineers and operations analysts seeking solutions to practical problems will find it a valuable resource, as will advanced undergraduate and graduate students in mathematics, statistics, operations research, industrial and electrical engineering, and computer science.

From the Back Cover:

This unique text collects more than 400 problems in combinatorics, derived distributions, discrete and continuous Markov chains, and models requiring a computer experimental approach. The first book to deal with simplified versions of models encountered in the contemporary statistical or engineering literature, Algorithmic Probability emphasizes correct interpretation of numerical results and visualization of the dynamics of stochastic processes. An overview of the required background material is followed by problem sets based on practical models, most of which require structural insight incorporated into computer routines to evaluate distributions of interest. A significant contribution to the field of applied probability, Algorithmic Probability is ideal both as a secondary text in probability courses and as a reference. Engineers and operations analysts seeking solutions to practical problems will find it a valuable resource, as will advanced undergraduate and graduate students in mathematics, statistics, operations research, industrial and electrical engineering and computer science.

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