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Ross S.M. — Introduction to probability models
Ross S.M. — Introduction to probability models



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Название: Introduction to probability models

Автор: Ross S.M.

Аннотация:

The sixth edition of the successful Introduction to Probability Models introduces elementary probability theory and the stochastic processes and is particularly well-suited to those applying probability theory to the study of phenomena in engineering, management science, the physical and social sciences, and operations research. Skillfully organized, Introduction to Probability Models covers all essential topics. Sheldon Ross, a talented and prolific textbook author, distinguishes this carefully and substantially revised book by his effort to develop in students an intuitive, and therefore lasting, grasp of probability theory. The seventh edition includes many new examples and exercises, with the majority of the new exercises being less demanding of the student. In addition, the text introduces stochastic processes, stressing applications, in an easily understood manner. There is a comprehensive introduction to the applied models of probability that stresses intuition. Both students and professors will agree that this is the most solid and widely used text for probability theory.


Язык: en

Рубрика: Математика/Вероятность/Стохастические процессы/

Статус предметного указателя: Готов указатель с номерами страниц

ed2k: ed2k stats

Издание: sixth edition

Год издания: 1997

Количество страниц: 669

Добавлена в каталог: 10.06.2005

Операции: Положить на полку | Скопировать ссылку для форума | Скопировать ID
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Предметный указатель
Semi — Markov process      379—381 407
Semi — Markov process limiting probabilities      379—380 381
Series system      476 482 512 521
Software reliability      275—277
Spanning trees      492
Standard normal distribution      36
Standard normal distribution table of probabilities      76
Stationary increments      250
Stationary probabilities      180
Stationary process      546
Stirling's approximation      167 171—172
Stochastic process      77
Stochastic process, continuous time process      77
Stochastic process, discrete time, process      77
Stochastic process, state space of      78
Stopping time      400 571
Stratified sampling      621
Strong law for renewal processes      357
Strong Law of Large Numbers      73—74
Structure function      476 515
Structure function dual      516
Sufficient statistic      143
Symmetric random walk      168
Symmetric random walk in higher dimensions      168—169
Symmetric random walk, relation to Brownian motion      523—524
Throughput rate      438
Tilted density function      609—610
Time reversible Markov chain, continuous case      330—331 332 334 346 348 434
Time reversible Markov chain, continuous case discrete case      201—202 206—208 231
Transient state      164 165 166 195—197
Transition probability function      313 338—340
Transition probability function, computation of      338—340 349
Transition probability matrix      158
Tree process      232
Truncated chain      334
Two dimensional Poisson process      300—301
Two dimensional Poisson process, simulation of      595—596
Two state continuous time Markov chain      318—320 336—338
Union of events      3
Unit normal distribution      see “Standard normal distribution”
Variance      43 44
Variance by conditioning      602—606
Variance of a sum of a random number of random variables      109—110
Variance of binomial random variables      52 62
Variance of exponential random variables      63
Variance of geometric random variables      100—111
Variance of normal random variables      43 64
Variance of Poisson random variables      62
Variance of sums of random variables      51
Variance, reduction techniques      598—613
Variance, tables of      65
Von Neumann algorithm      578—580
Wald's equation      400—401 571—572
Weak law of large numbers      88
Weakly stationary      see “Second order stationary”
Weibull distribution      498—499
White noise      541—542
Wiener process      see “Brownian motion”
Work in queue      442—443
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