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E-raamat: Markov Processes

(Sweet Briar College, Virginia, USA)
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Clear, rigorous, and intuitive, Markov Processes provides a bridge from an undergraduate probability course to a course in stochastic processes and also as a reference for those that want to see detailed proofs of the theorems of Markov processes. It contains copious computational examples that motivate and illustrate the theorems. The text is designed to be understandable to students who have taken an undergraduate probability course without needing an instructor to fill in any gaps.

The book begins with a review of basic probability, then covers the case of finite state, discrete time Markov processes. Building on this, the text deals with the discrete time, infinite state case and provides background for continuous Markov processes with exponential random variables and Poisson processes. It presents continuous Markov processes which include the basic material of Kolmogorovs equations, infinitesimal generators, and explosions. The book concludes with coverage of both discrete and continuous reversible Markov chains.

While Markov processes are touched on in probability courses, this book offers the opportunity to concentrate on the topic when additional study is required. It discusses how Markov processes are applied in a number of fields, including economics, physics, and mathematical biology. The book fills the gap between a calculus based probability course, normally taken as an upper level undergraduate course, and a course in stochastic processes, which is typically a graduate course.

Arvustused

"All chapters are followed by exercises that render this text-book attractive for teachers" Zentralblatt MATH

"Kirkwoodhas published another significant mathematics monograph."

"Suitable for audiences who strive to grasp the fundamental concepts of various types of Markov processes or to prepare for learning advanced stochastic processes the monograph can serve as a textbook since it provides essential examples and exercise problems applied in economics, finance, engineering, physics, and biology." S-T. Kim, North Carolina A&T State University

Preface xi
1 Review of Probability 1(40)
Short History
1(1)
Review of Basic Probability Definitions
2(2)
Some Common Probability Distributions
4(4)
Bernoulli Distribution
5(1)
Binomial Distribution
5(1)
Geometric Distribution
6(1)
Negative Binomial Distribution
7(1)
Poisson Distribution
8(1)
Properties of a Probability Distribution
8(9)
Conditional Probability
9(2)
Independent Events
11(2)
Random Variables
13(1)
Expected Value of a Random Variable
14(3)
Properties of the Expected Value
17(1)
Expected Value of a Random Variable with Common Distributions
17(12)
Bernoulli Distribution
17(1)
Binomial Distribution
18(1)
Geometric Distribution
18(1)
Negative Binomial Distribution
19(1)
Poisson Distribution
20(1)
Functions of a Random Variable
21(3)
Joint Distributions
24(5)
Generating Functions
29(3)
Probability Generating Functions
29(3)
Moment Generating Functions
32(1)
Exercises
33(8)
2 Discrete-Time, Finite-State Markov Chains 41(100)
Introduction
41(1)
Notation
41(1)
Transition Matrices
42(9)
Directed Graphs: Examples of Markov Chains
51(1)
Random Walk with Reflecting Boundaries
52(1)
Gambler's Ruin
53(1)
Ehrenfest Model
54(1)
Central Problem of Markov Chains
55(1)
Condition to Ensure a Unique Equilibrium State
56(8)
Finding the Equilibrium State
64(2)
Transient and Recurrent States
66(3)
Indicator Functions
69(5)
Perron-Frobenius Theorem
74(6)
Absorbing Markov Chains
80(11)
Mean First Passage Time
91(2)
Mean Recurrence Time and the Equilibrium State
93(8)
Fundamental Matrix for Regular Markov Chains
101(10)
Dividing a Markov Chain into Equivalence Classes
111(4)
Periodic Markov Chains
115(11)
Reducible Markov Chains
126(3)
Summary
129(3)
Exercises
132(9)
3 Discrete-Time, Infinite-State Markov Chains 141(54)
Renewal Processes
141(14)
Delayed Renewal Processes
155(1)
Equilibrium State for Countable Markov Chains
156(1)
Physical Interpretation of the Equilibrium State
157(1)
Null Recurrent versus Positive Recurrent States
157(10)
Difference Equations
167(7)
Branching Processes
174(8)
Random Walk in Zd
182(8)
Exercises
190(5)
4 Exponential Distribution and Poisson Process 195(30)
Continuous Random Variables
195(1)
Cumulative Distribution Function (Continuous Case)
196(2)
Exponential Distribution
198(2)
o(h) Functions
200(1)
Exponential Distribution as a Model for Arrivals
200(5)
Memoryless Random Variables
205(9)
Poisson Process
214(5)
Poisson Processes with Occurrences of Two Types
219(3)
Exercises
222(3)
5 Continuous-Time Markov Chains 225(72)
Introduction
225(3)
Generators of Continuous Markov Chains: The Kolmogorov Forward and Backward Equations
228(17)
Connections of the Infinitesimal Generator, the Embedded
Markov Chain, Transition Rates, and the Stationary Distribution
238(7)
Connection between the Steady State of a Continuous Markov Chain and the Steady State of the Embedded Matrix
245(3)
Explosions
248(4)
Birth and Birth-Death Processes
252(4)
Birth Process (Forward Equation)
253(2)
Birth Process (Backward Equation)
255(1)
Birth and Death Processes
256(14)
Forward Equations for Birth-Death Processes
257(2)
Birth-Death Processes: Backward Equations
259(1)
Recurrence and Transience in Birth-Death Processes
260(10)
Queuing Models
270(6)
M/M/1 Model
271(2)
M/M/1/K Queue: One Server (Size of the Queue Is Limited)
273(3)
Detailed Balance Equations
276(14)
M/M/K Model
279(6)
M/M/c/K Queue: c Servers (Size of the Queue Is Limited to K)
285(4)
M/M/infinity Model
289(1)
Exercises
290(7)
6 Reversible Markov Chains 297(24)
Random Walks on Weighted Graphs
312(2)
Discrete Time Birth-Death Process as a Reversible Markov Chain
314(1)
Continuous-Time Reversible Markov Chains
315(3)
Exercises
318(3)
Bibliography 321(2)
Index 323
James R. Kirkwood