Elements of applied stochastic processes

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Elements of applied stochastic processes / U. Narayan Bhat

資料種別:: 図書
出版情報:: New York : J. Wiley, c1972
形態:: xvi, 414 p. ; 23 cm
シリーズ名:: Wiley series in probability and mathematical statistics <BA00019013>
著者名:: Bhat, U. Narayan, 1933- <DA01064745>
ISBN:: 9780471071990 [0471071994]
書誌ID:: BA03083154

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書誌詳細

注記:

Includes bibliographies

主題:

Stochastic processes

言語:

英語

目次情報:

Preface

Stochastic Processes: Description and Definition / 1：

Introduction / 1.1：

Description and Definition / 1.2：

Probability Distributions / 1.3：

The Markov Process / 1.4：

The Renewal Process / 1.5：

The Stationary Process / 1.6：

A Plan for the Remaining Chapters / 1.7：

References

Exercises

Elementary Review Exercises

Advanced Review Exercises

Markov Chains / 2：

The n-Step Transition Probability Matrix / 2.1：

Classification of States / 2.3：

A Canonical Representation of the Transition Probability Matrix / 2.4：

Classification of States in Practice / 2.5：

Finite Markov Chains with Transient States / 2.6：

Irreducible Markov Chains with Ergodic States / 3：

Transient Behavior / 3.1：

Limiting Behavior / 3.2：

First Passage and Related Results / 3.3：

Branching Processes and other Special Topics / 4：

Branching Processes / 4.1：

Markov Chains of Order Higher than 1 / 4.2：

Lumpable Markov Chains / 4.3：

Reversed Markov Chains / 4.4：

Statistical Inference for Markov Chains / 5：

Estimation of the Elements in a Transition Probability Matrix / 5.1：

Hypothesis Testing Issues for Markov Chains / 5.2：

Inference From Partially Observable Markov Chains / 5.3：

Statistical Inference for Branching Processes / 5.4：

Additional Comments / 5.5：

Applied Markov Chains / 6：

Queueing Models / 6.1：

Inventory Systems / 6.2：

Storage Models / 6.3：

Industrial Mobility of Labor / 6.4：

Educational Advancement / 6.5：

Human Resource Management / 6.6：

Term Structure / 6.7：

Income Determination under Uncertainty / 6.8：

A Markov Decision Process / 6.9：

Simple Markov Processes / 7：

Examples / 7.1：

Markov Processes: General Properties / 7.2：

The Poisson Process / 7.3：

The Pure Birth Process / 7.4：

The Pure Death Process / 7.5：

Birth and Death Processes / 7.6：

Limiting Distributions / 7.7：

Markovian Networks / 7.8：

Additional Examples / 7.9：

Statistical Inference for Simple Markov Processes / 8：

Estimation of Parameters / 8.1：

Hypothesis Testing for Simple Markov Processes / 8.2：

Statistical Inference for Queues / 8.3：

Applied Markov Processes / 8.4：

The Machine Interference Problem / 9.1：

Queueing Networks / 9.3：

Flexible Manufacturing Systems / 9.4：

Reliability Models / 9.5：

Markovian Combat Models / 9.7：

Stochastic Models for Social Networks / 9.8：

Recovery, Relapse, and Death Due to Disease / 9.9：

Renewal Processes / 10：

Renewal Processes when Time is Discrete / 10.1：

Renewal Processes when Time is Continuous / 10.3：

Alternating Renewal Processes / 10.4：

Markov Renewal Processes (Semi-Markov Processes) / 10.5：

Renewal Reward Processes / 10.6：

Statistical Inference for Renewal Processes / 10.7：

Stationary Processes and Time Series Analysis / 10.8：

Definition / 11.1：

Some Examples / 11.2：

Ergodic Theorems / 11.3：

Covariance Stationary Processes in the Frequency Domain / 11.4：

Time Series Analysis: Introduction / 11.5：

Stochastic Models for Time Series / 11.6：

The Autoregressive Process / 11.7：

The Moving Average Process / 11.8：

A Mixed Autoregressive Moving Average Process / 11.9：

Autoregressive Integrated Moving Average Processes / 11.10：

Time Series Analysis in the Time Domain / 11.11：

Spectral Analysis of Time Series Data / 11.12：

Simulation and Markov Chain Monte Carlo / 12：

Simulation / 12.1：

Markov Chain Monte Carlo / 12.3：

Answers to Selected Exercises

Appendix

Author Index

Subject Index

Preface

Stochastic Processes: Description and Definition / 1：

Introduction / 1.1：

Description and Definition / 1.2：

Probability Distributions / 1.3：

The Markov Process / 1.4：

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