Elements of Applied Stochastic Processes

ISBN-10: 0471414425

ISBN-13: 9780471414421

Edition: 3rd 2002 (Revised)

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Description: This edition improves on the last by organising the material into a more teachable format. It provides in-depth coverage of Markov chains and simple Markov process and gives added emphasis to statistical inference in stochastic processes.

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Book details

List price: $191.00
Edition: 3rd
Copyright year: 2002
Publisher: John Wiley & Sons, Incorporated
Publication date: 9/6/2002
Binding: Hardcover
Pages: 488
Size: 6.25" wide x 9.25" long x 1.25" tall
Weight: 1.738
Language: English

Preface
Stochastic Processes: Description and Definition
Introduction
Description and Definition
Probability Distributions
The Markov Process
The Renewal Process
The Stationary Process
A Plan for the Remaining Chapters
References
Exercises
Elementary Review Exercises
Advanced Review Exercises
Markov Chains
Introduction
The n-Step Transition Probability Matrix
Classification of States
A Canonical Representation of the Transition Probability Matrix
Classification of States in Practice
Finite Markov Chains with Transient States
References
Exercises
Irreducible Markov Chains with Ergodic States
Transient Behavior
Limiting Behavior
First Passage and Related Results
References
Exercises
Branching Processes and other Special Topics
Branching Processes
Markov Chains of Order Higher than 1
Lumpable Markov Chains
Reversed Markov Chains
References
Exercises
Statistical Inference for Markov Chains
Estimation of the Elements in a Transition Probability Matrix
Hypothesis Testing Issues for Markov Chains
Inference From Partially Observable Markov Chains
Statistical Inference for Branching Processes
Additional Comments
References
Exercises
Applied Markov Chains
Queueing Models
Inventory Systems
Storage Models
Industrial Mobility of Labor
Educational Advancement
Human Resource Management
Term Structure
Income Determination under Uncertainty
A Markov Decision Process
References
Simple Markov Processes
Examples
Markov Processes: General Properties
The Poisson Process
The Pure Birth Process
The Pure Death Process
Birth and Death Processes
Limiting Distributions
Markovian Networks
Additional Examples
References
Exercises
Statistical Inference for Simple Markov Processes
Estimation of Parameters
Hypothesis Testing for Simple Markov Processes
Statistical Inference for Queues
Additional Examples
References
Exercises
Applied Markov Processes
Queueing Models
The Machine Interference Problem
Queueing Networks
Flexible Manufacturing Systems
Inventory Systems
Reliability Models
Markovian Combat Models
Stochastic Models for Social Networks
Recovery, Relapse, and Death Due to Disease
References
Renewal Processes
Introduction
Renewal Processes when Time is Discrete
Renewal Processes when Time is Continuous
Alternating Renewal Processes
Markov Renewal Processes (Semi-Markov Processes)
Renewal Reward Processes
Statistical Inference for Renewal Processes
Additional Examples
References
Exercises
Stationary Processes and Time Series Analysis
Definition
Some Examples
Ergodic Theorems
Covariance Stationary Processes in the Frequency Domain
Time Series Analysis: Introduction
Stochastic Models for Time Series
The Autoregressive Process
The Moving Average Process
A Mixed Autoregressive Moving Average Process
Autoregressive Integrated Moving Average Processes
Time Series Analysis in the Time Domain
Spectral Analysis of Time Series Data
References
Exercises
Simulation and Markov Chain Monte Carlo
Introduction
Simulation
Markov Chain Monte Carlo
References
Answers to Selected Exercises
Appendix
Author Index
Subject Index
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