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E-raamat: Markov Chains and Stochastic Stability

Prologue by , , (University of Illinois, Urbana-Champaign)
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Meyn & Tweedie is back! The bible on Markov chains in general state spaces has been brought up to date to reflect developments in the field since 1996 - many of them sparked by publication of the first edition. The pursuit of more efficient simulation algorithms for complex Markovian models, or algorithms for computation of optimal policies for controlled Markov models, has opened new directions for research on Markov chains. As a result, new applications have emerged across a wide range of topics including optimisation, statistics, and economics. New commentary and an epilogue by Sean Meyn summarise recent developments and references have been fully updated. This second edition reflects the same discipline and style that marked out the original and helped it to become a classic: proofs are rigorous and concise, the range of applications is broad and knowledgeable, and key ideas are accessible to practitioners with limited mathematical background.

New up-to-date edition of this influential classic - the bible on Markov chains in general state spaces is back.

Arvustused

'This second edition remains true to the remarkable standards of scholarship established by the first edition it will no doubt be a very welcome addition to the literature.' Peter W. Glynn, Prologue to the Second Edition

Muu info

New up-to-date edition of this influential classic - the bible on Markov chains in general state spaces is back.
List of flgures
xi
Prologue to the second edition xiii
Peter W. Glynn
Preface to the second xvii
Sean Meyn
Preface to the first edition xxi
I COMMUNICATION and REGENERATION
1(168)
Heuristics
3(18)
A range of Markovian environments
3(3)
Basic models in practice
6(7)
Stochastic stability for Markov models
13(6)
Commentary
19(2)
Markov models
21(27)
Markov models in time series
22(4)
Nonlinear state space models
26(7)
Models in control and systems theory
33(5)
Markov models with regeneration times
38(8)
Commentary
46(2)
Transition probabilities
48(27)
Defining a Markovian process
49(2)
Foundations on a countable space
51(3)
Specific transition matrices
54(5)
Foundations for general state space chains
59(8)
Building transition kernels for specific models
67(5)
Commentary
72(3)
Irreducibility
75(21)
Communication and irreducibility: Countable spaces
76(5)
Irreducibility
81(6)
Irreducibility for random walk models
87(2)
Irreducible linear models
89(4)
Commentary
93(3)
Pseudo-atoms
96(27)
Splitting irreducible chains
97(5)
Small sets
102(4)
Small sets for specific models
106(4)
Cyclic behavior
110(5)
Petite sets and sampled chains
115(6)
Commentary
121(2)
Topology and continuity
123(23)
Feller properties and forms of stability
125(5)
T-chains
130(4)
Continuous components for specific models
134(5)
e-Chains
139(5)
Commentary
144(2)
The nonlinear state space model
146(23)
Forward accessibility and continuous components
147(7)
Minimal sets and irreducibility
154(3)
Periodicity for nonlinear state space models
157(4)
Forward accessible examples
161(2)
Equicontinuity and the nonlinear state space model
163(2)
Commentary
165(4)
II STABILITY STRUCTURES
169(142)
Transience and recurrence
171(28)
Classifying chains on countable spaces
173(4)
Classifying irreducible chains
177(5)
Recurrence and transience relationships
182(5)
Classification using drift criteria
187(6)
Classifying ranodom walk on R+
193(4)
Commentary
197(2)
Harris and topological recurrence
199(30)
Harris recurrence
201(5)
Non-evanescent and recurrent chains
206(2)
Topologically recurrent and transient states
208(5)
Criteria for stability on a topological space
213(5)
Stochastic comparison and increment analysis
218(10)
Commentary
228(1)
The existence of π
229(27)
Stationarity and invariance
230(4)
The existence of π: chains with atoms
234(2)
Invariant measures for countable space models
236(5)
The existence of π: irreducible chains
241(6)
Invariant measures for general models
247(6)
Commentary
253(3)
Drift and regularity
256(32)
Regular chains
258(3)
Drift, hitting times and deterministic models
261(2)
Dirft criteria for rugularity
263(9)
Using the resularity criteria
272(6)
Evaluating non-positivity
278(7)
Commentary
285(3)
Invariance and tightness
288(23)
Chains bounded in probability
289(3)
Generalized sampling and invariant measures
292(6)
The existence of a Q-finite invariant measure
298(2)
Invariant measures for e-chains
300(5)
Establishing boundedness in probability
305(3)
Commentary
308(3)
III CONVERGENCE
311(218)
Ergodicity
311(25)
Ergodic chains on countable spaces
316(4)
Renewal and regeneration
320(6)
Ergodicity of positive Harris chains
326(3)
Sums of transition probabilites
329(5)
Commentary
334(2)
f-Ergodicity and f-regularity
336(26)
f-Properties: chains with atoms
338(4)
f-Regularity and drift
342(7)
f-Ergodicity for general chains
349(3)
f-Ergodicity of specific models
352(2)
A key renewal theorem
354(5)
Commentary
359(3)
Geometric ergodicity
362(30)
Geometric properties: chains with atoms
364(8)
Kendall sets and drift criteria
372(8)
f-Gemetric regularity of φ and its skeleton
380(8)
f-Geometric ergodicity for general chains
388(1)
Simple random walkand liner models
388(2)
Commentary
390(2)
V-Uniform ergodicity
392(29)
OPerator norm convergence
395(5)
Uniform ergodicity
400(7)
Geometric ergodicity and increment analysis
407(4)
Models from queueing theory
411(3)
Autoregressive and state space models
414(4)
Commentary
418(3)
Sample paths and limit theorems
421(41)
Invariant σ-field and the LLN
423(5)
Ergodic theorems for chains possessing an atom
428(5)
General Harris chains
433(10)
The functional CLT
443(7)
Criteria for the CLT and the LIL
450(4)
Applications
454(2)
Commentary
456(6)
Positivity
462(20)
Null recurrent chains
464(5)
Characterizing positivity using Pn
469(2)
Positivity and T-chains
471(2)
Positivity and e-chains
473(4)
The LLN for e-chains
477(3)
Commentary
480(2)
Generalized classification criteria
482(28)
State-dependent dreifts
483(8)
History-dependent drift criteria
491(7)
Mixed drift criteria
498(10)
Commentary
508(2)
Epilogue to the second edition
510(19)
Geometric ergodicity and specral theroy
510(11)
Simulation and MCMC
521(2)
Continuous timemodels
523(6)
IV APPENDICES
529(38)
Mud maps
532(6)
Recurrence versus transience
532(2)
Positivity versus mullity
534(2)
Convergence properties
536(2)
Festing for stability
538(5)
Glossary of drift conditions
538(2)
The scalar SETAR model: a complete classification
540(3)
Glossary of model assumptions
543(9)
Regenerative models
543(3)
State space models
546(6)
Some mathematical background
552(15)
Some measure theory
552(3)
Some probability theory
555(1)
Some topology
556(1)
Some real analysis
557(1)
Convergence concepts for measures
558(3)
Some martingale theory
561(2)
Some results on sequences and numbers
563(4)
Bibliography
567(20)
Indexes
587
General index
587
Symbols
593
Sean Meyn is a professor in the Department of Electrical and Computer Engineering and director of the Division and Control Laboratory of the Coordinated Science Laboratory at the University of Illinois. He has served on the editorial boards of several journals in areas of systems and control and applied probability. Richard L. Tweedie was Professor and Head of the Division of Biostatistics at the University of Minnesota before his death in 2001.