The Resource Markov chains : analytic and Monte Carlo computations, Carl Graham
Markov chains : analytic and Monte Carlo computations, Carl Graham
Resource Information
The item Markov chains : analytic and Monte Carlo computations, Carl Graham represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of Missouri Libraries.This item is available to borrow from 2 library branches.
Resource Information
The item Markov chains : analytic and Monte Carlo computations, Carl Graham represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of Missouri Libraries.
This item is available to borrow from 2 library branches.
 Summary
 Markov Chains: Analytic and Monte Carlo Computations introduces the main notions related to Markov chains and provides explanations on how to characterize, simulate, and recognize them. Starting with basic notions, this book leads progressively to advanced and recent topics in the field, allowing the reader to master the main aspects of the classical theory. This book also features: Numerous exercises with solutions as well as extended case studies. A detailed and rigorous presentation of Markov chains with discrete time and state space
 Language
 eng
 Extent
 1 online resource
 Contents

 Cover; Title Page; Copyright; Contents; Preface; List of Figures; Nomenclature; Introduction; Chapter 1 First steps; 1.1 Preliminaries; 1.2 First properties of Markov chains; 1.2.1 Markov chains, finitedimensional marginals, and laws; 1.2.2 Transition matrix action and matrix notation; 1.2.3 Random recursion and simulation; 1.2.4 Recursion for the instantaneous laws, invariant laws; 1.3 Natural duality: algebraic approach; 1.3.1 Complex eigenvalues and spectrum; 1.3.2 Doeblin condition and strong irreducibility; 1.3.3 Finite state space Markov chains; 1.4 Detailed examples
 2.3 Detailed examples2.3.1 Gambler's ruin; 2.3.2 Unilateral hitting time for a random walk; 2.3.3 Exit time from a box; 2.3.4 Branching process; 2.3.5 Word search; Exercises; Chapter 3 Transience and recurrence; 3.1 Sample paths and state space; 3.1.1 Communication and closed irreducible classes; 3.1.2 Transience and recurrence, recurrent class decomposition; 3.1.3 Detailed examples; 3.2 Invariant measures and recurrence; 3.2.1 Invariant laws and measures; 3.2.2 Canonical invariant measure; 3.2.3 Positive recurrence, invariant law criterion; 3.2.4 Detailed examples; 3.3 Complements
 3.3.1 Hitting times and superharmonic functions3.3.2 Lyapunov functions; 3.3.3 Time reversal, reversibility, and adjoint chain; 3.3.4 Birthanddeath chains; Exercises; Chapter 4 Longtime behavior; 4.1 Path regeneration and convergence; 4.1.1 Pointwise ergodic theorem, extensions; 4.1.2 Central limit theorem for Markov chains; 4.1.3 Detailed examples; 4.2 Longtime behavior of the instantaneous laws; 4.2.1 Period and aperiodic classes; 4.2.2 Coupling of Markov chains and convergence in law; 4.2.3 Detailed examples; 4.3 Elements on the rate of convergence for laws
 4.3.1 The Hilbert space framework4.3.2 Dirichlet form, spectral gap, and exponential bounds; 4.3.3 Spectral theory for reversible matrices; 4.3.4 Continuoustime Markov chains; Exercises; Chapter 5 Monte Carlo methods; 5.1 Approximate solution of the Dirichlet problem; 5.1.1 General principles; 5.1.2 Heat equation in equilibrium; 5.1.3 Heat equation out of equilibrium; 5.1.4 Parabolic partial differential equations; 5.2 Invariant law simulation; 5.2.1 Monte Carlo methods and ergodic theorems; 5.2.2 Metropolis algorithm, Gibbs law, and simulated annealing
 Isbn
 9781118882696
 Label
 Markov chains : analytic and Monte Carlo computations
 Title
 Markov chains
 Title remainder
 analytic and Monte Carlo computations
 Statement of responsibility
 Carl Graham
 Language
 eng
 Summary
 Markov Chains: Analytic and Monte Carlo Computations introduces the main notions related to Markov chains and provides explanations on how to characterize, simulate, and recognize them. Starting with basic notions, this book leads progressively to advanced and recent topics in the field, allowing the reader to master the main aspects of the classical theory. This book also features: Numerous exercises with solutions as well as extended case studies. A detailed and rigorous presentation of Markov chains with discrete time and state space
 Cataloging source
 DLC
 http://library.link/vocab/creatorName
 Graham, C.
 Dewey number
 519.2/33
 Index
 index present
 LC call number
 QA274.7
 Literary form
 non fiction
 Nature of contents

 dictionaries
 bibliography
 Series statement
 Wiley series in probability and statistics
 http://library.link/vocab/subjectName

 Markov processes
 Monte Carlo method
 Numerical calculations
 MATHEMATICS
 MATHEMATICS
 Markov processes
 Monte Carlo method
 Numerical calculations
 Label
 Markov chains : analytic and Monte Carlo computations, Carl Graham
 Bibliography note
 Includes bibliographical references and index
 Carrier category
 online resource
 Carrier category code

 cr
 Carrier MARC source
 rdacarrier
 Content category
 text
 Content type code

 txt
 Content type MARC source
 rdacontent
 Contents

 Cover; Title Page; Copyright; Contents; Preface; List of Figures; Nomenclature; Introduction; Chapter 1 First steps; 1.1 Preliminaries; 1.2 First properties of Markov chains; 1.2.1 Markov chains, finitedimensional marginals, and laws; 1.2.2 Transition matrix action and matrix notation; 1.2.3 Random recursion and simulation; 1.2.4 Recursion for the instantaneous laws, invariant laws; 1.3 Natural duality: algebraic approach; 1.3.1 Complex eigenvalues and spectrum; 1.3.2 Doeblin condition and strong irreducibility; 1.3.3 Finite state space Markov chains; 1.4 Detailed examples
 2.3 Detailed examples2.3.1 Gambler's ruin; 2.3.2 Unilateral hitting time for a random walk; 2.3.3 Exit time from a box; 2.3.4 Branching process; 2.3.5 Word search; Exercises; Chapter 3 Transience and recurrence; 3.1 Sample paths and state space; 3.1.1 Communication and closed irreducible classes; 3.1.2 Transience and recurrence, recurrent class decomposition; 3.1.3 Detailed examples; 3.2 Invariant measures and recurrence; 3.2.1 Invariant laws and measures; 3.2.2 Canonical invariant measure; 3.2.3 Positive recurrence, invariant law criterion; 3.2.4 Detailed examples; 3.3 Complements
 3.3.1 Hitting times and superharmonic functions3.3.2 Lyapunov functions; 3.3.3 Time reversal, reversibility, and adjoint chain; 3.3.4 Birthanddeath chains; Exercises; Chapter 4 Longtime behavior; 4.1 Path regeneration and convergence; 4.1.1 Pointwise ergodic theorem, extensions; 4.1.2 Central limit theorem for Markov chains; 4.1.3 Detailed examples; 4.2 Longtime behavior of the instantaneous laws; 4.2.1 Period and aperiodic classes; 4.2.2 Coupling of Markov chains and convergence in law; 4.2.3 Detailed examples; 4.3 Elements on the rate of convergence for laws
 4.3.1 The Hilbert space framework4.3.2 Dirichlet form, spectral gap, and exponential bounds; 4.3.3 Spectral theory for reversible matrices; 4.3.4 Continuoustime Markov chains; Exercises; Chapter 5 Monte Carlo methods; 5.1 Approximate solution of the Dirichlet problem; 5.1.1 General principles; 5.1.2 Heat equation in equilibrium; 5.1.3 Heat equation out of equilibrium; 5.1.4 Parabolic partial differential equations; 5.2 Invariant law simulation; 5.2.1 Monte Carlo methods and ergodic theorems; 5.2.2 Metropolis algorithm, Gibbs law, and simulated annealing
 Control code
 865574978
 Extent
 1 online resource
 Form of item
 online
 Isbn
 9781118882696
 Lccn
 2013050092
 Media category
 computer
 Media MARC source
 rdamedia
 Media type code

 c
 http://library.link/vocab/ext/overdrive/overdriveId
 cl0500000628
 Specific material designation
 remote
 System control number
 (OCoLC)865574978
 Label
 Markov chains : analytic and Monte Carlo computations, Carl Graham
 Bibliography note
 Includes bibliographical references and index
 Carrier category
 online resource
 Carrier category code

 cr
 Carrier MARC source
 rdacarrier
 Content category
 text
 Content type code

 txt
 Content type MARC source
 rdacontent
 Contents

 Cover; Title Page; Copyright; Contents; Preface; List of Figures; Nomenclature; Introduction; Chapter 1 First steps; 1.1 Preliminaries; 1.2 First properties of Markov chains; 1.2.1 Markov chains, finitedimensional marginals, and laws; 1.2.2 Transition matrix action and matrix notation; 1.2.3 Random recursion and simulation; 1.2.4 Recursion for the instantaneous laws, invariant laws; 1.3 Natural duality: algebraic approach; 1.3.1 Complex eigenvalues and spectrum; 1.3.2 Doeblin condition and strong irreducibility; 1.3.3 Finite state space Markov chains; 1.4 Detailed examples
 2.3 Detailed examples2.3.1 Gambler's ruin; 2.3.2 Unilateral hitting time for a random walk; 2.3.3 Exit time from a box; 2.3.4 Branching process; 2.3.5 Word search; Exercises; Chapter 3 Transience and recurrence; 3.1 Sample paths and state space; 3.1.1 Communication and closed irreducible classes; 3.1.2 Transience and recurrence, recurrent class decomposition; 3.1.3 Detailed examples; 3.2 Invariant measures and recurrence; 3.2.1 Invariant laws and measures; 3.2.2 Canonical invariant measure; 3.2.3 Positive recurrence, invariant law criterion; 3.2.4 Detailed examples; 3.3 Complements
 3.3.1 Hitting times and superharmonic functions3.3.2 Lyapunov functions; 3.3.3 Time reversal, reversibility, and adjoint chain; 3.3.4 Birthanddeath chains; Exercises; Chapter 4 Longtime behavior; 4.1 Path regeneration and convergence; 4.1.1 Pointwise ergodic theorem, extensions; 4.1.2 Central limit theorem for Markov chains; 4.1.3 Detailed examples; 4.2 Longtime behavior of the instantaneous laws; 4.2.1 Period and aperiodic classes; 4.2.2 Coupling of Markov chains and convergence in law; 4.2.3 Detailed examples; 4.3 Elements on the rate of convergence for laws
 4.3.1 The Hilbert space framework4.3.2 Dirichlet form, spectral gap, and exponential bounds; 4.3.3 Spectral theory for reversible matrices; 4.3.4 Continuoustime Markov chains; Exercises; Chapter 5 Monte Carlo methods; 5.1 Approximate solution of the Dirichlet problem; 5.1.1 General principles; 5.1.2 Heat equation in equilibrium; 5.1.3 Heat equation out of equilibrium; 5.1.4 Parabolic partial differential equations; 5.2 Invariant law simulation; 5.2.1 Monte Carlo methods and ergodic theorems; 5.2.2 Metropolis algorithm, Gibbs law, and simulated annealing
 Control code
 865574978
 Extent
 1 online resource
 Form of item
 online
 Isbn
 9781118882696
 Lccn
 2013050092
 Media category
 computer
 Media MARC source
 rdamedia
 Media type code

 c
 http://library.link/vocab/ext/overdrive/overdriveId
 cl0500000628
 Specific material designation
 remote
 System control number
 (OCoLC)865574978
Subject
 Electronic books
 MATHEMATICS  Applied
 MATHEMATICS  Probability & Statistics  General
 Markov processes
 Markov processes
 Monte Carlo method
 Monte Carlo method
 Numerical calculations
 Numerical calculations
Genre
Member of
 Wiley series in probability and statistics
 O'Reilly Safari Learning Platform: Academic edition
 Ebook Central Academic Complete
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