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: |