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Discrete-Time Semi-Markov Random Evolutions and Their Applications / / by Nikolaos Limnios, Anatoliy Swishchuk
Discrete-Time Semi-Markov Random Evolutions and Their Applications / / by Nikolaos Limnios, Anatoliy Swishchuk
Autore Limnios Nikolaos
Edizione [1st ed. 2023.]
Pubbl/distr/stampa Cham : , : Springer Nature Switzerland : , : Imprint : Birkhäuser, , 2023
Descrizione fisica 1 online resource (206 pages)
Disciplina 519.233
Altri autori (Persone) SwishchukAnatoliy
Collana Probability and Its Applications
Soggetto topico Stochastic processes
Probabilities
Mathematical statistics
Dynamics
Stochastic Processes
Probability Theory
Mathematical Statistics
Applied Probability
Dynamical Systems
Stochastic Systems and Control
Processos de Markov
Sistemes de temps discret
Soggetto genere / forma Llibres electrònics
ISBN 3-031-33429-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Contents -- Acronyms -- Notation -- 1 Discrete-Time Stochastic Calculus in Banach Space -- 1.1 Introduction -- 1.2 Random Elements and Discrete-Time Martingales in a Banach Space -- 1.3 Martingale Characterization of Markov and Semi-Markov Chains -- 1.3.1 Martingale Characterization of Markov Chains -- 1.3.2 Martingale Characterization of Markov Processes -- 1.3.3 Martingale Characterization of Semi-Markov Processes -- 1.4 Operator Semigroups and Their Generators -- 1.5 Martingale Problem in a Banach Space -- 1.6 Weak Convergence in a Banach Space -- 1.7 Reducible-Invertible Operators and Their Perturbations -- 1.7.1 Reducible-Invertible Operators -- 1.7.2 Perturbation of Reducible-Invertible Operators -- 2 Discrete-Time Semi-Markov Chains -- 2.1 Introduction -- 2.2 Semi-Markov Chains -- 2.2.1 Definitions -- 2.2.2 Classification of States -- 2.2.3 Markov Renewal Equation and Theorem -- 2.3 Discrete- and Continuous-Time Connection -- 2.4 Compensating Operator and Martingales -- 2.5 Stationary Phase Merging -- 2.6 Semi-Markov Chains in Merging State Space -- 2.6.1 The Ergodic Case -- 2.6.2 The Non-ergodic Case -- 2.7 Concluding Remarks -- 3 Discrete-Time Semi-Markov Random Evolutions -- 3.1 Introduction -- 3.2 Discrete-time Random Evolution with Underlying Markov Chain -- 3.3 Definition and Properties of DTSMRE -- 3.4 Discrete-Time Stochastic Systems -- 3.4.1 Additive Functionals -- 3.4.2 Geometric Markov Renewal Chains -- 3.4.3 Dynamical Systems -- 3.5 Discrete-Time Stochastic Systems in Series Scheme -- 3.6 Concluding Remarks -- 4 Weak Convergence of DTSMRE in Series Scheme -- 4.1 Introduction -- 4.2 Weak Convergence Results -- 4.2.1 Averaging -- 4.2.2 Diffusion Approximation -- 4.2.3 Normal Deviations -- 4.2.4 Rates of Convergence in the Limit Theorems -- 4.3 Proof of Theorems -- 4.3.1 Proof of Theorem 4.1.
4.3.2 Proof of Theorem 4.2 -- 4.3.3 Proof of Theorem 4.3 -- 4.3.4 Proof of Proposition 4.1 -- 4.4 Applications of the Limit Theorems to Stochastic Systems -- 4.4.1 Additive Functionals -- 4.4.2 Geometric Markov Renewal Processes -- 4.4.3 Dynamical Systems -- 4.4.4 Estimation of the Stationary Distribution -- 4.4.5 U-Statistics -- 4.4.6 Rates of Convergence for Stochastic Systems -- 4.5 Concluding Remarks -- 5 DTSMRE in Reduced Random Media -- 5.1 Introduction -- 5.2 Definition and Properties -- 5.3 Average and Diffusion Approximation -- 5.3.1 Averaging -- 5.3.2 Diffusion Approximation -- 5.3.3 Normal Deviations -- 5.4 Proof of Theorems -- 5.4.1 Proof of Theorem 5.1 -- 5.4.2 Proof of Theorem 5.2 -- 5.5 Application to Stochastic Systems -- 5.5.1 Additive Functionals -- 5.5.2 Dynamical Systems -- 5.5.3 Geometric Markov Renewal Chains -- 5.5.4 U-Statistics -- 5.6 Concluding Remarks -- 6 Controlled Discrete-Time Semi-Markov Random Evolutions -- 6.1 Introduction -- 6.2 Controlled Discrete-Time Semi-Markov Random Evolutions -- 6.2.1 Definition of CDTSMREs -- 6.2.2 Examples -- 6.2.3 Dynamic Programming for Controlled Models -- 6.3 Limit Theorems for Controlled Semi-Markov Random Evolutions -- 6.3.1 Averaging of CDTSMREs -- 6.3.2 Diffusion Approximation of DTSMREs -- 6.3.3 Normal Approximation -- 6.4 Applications to Stochastic Systems -- 6.4.1 Controlled Additive Functionals -- 6.4.2 Controlled Geometric Markov Renewal Processes -- 6.4.3 Controlled Dynamical Systems -- 6.4.4 The Dynamic Programming Equations for Limiting Models in Diffusion Approximation -- 6.4.4.1 DPE/HJB Equation for the Limiting CAF in DA (see Sect.6.4.1) -- 6.4.4.2 DPE/HJB Equation for the Limiting CGMRP in DA (see Sect.6.4.2) -- 6.4.4.3 DPE/HJB Equation for the Limiting CDS in DA (see Sect.6.4.3) -- 6.5 Solution of Merton Problem for the Limiting CGMRP in DA -- 6.5.1 Introduction.
6.5.2 Utility Function -- 6.5.3 Value Function or Performance Criterion -- 6.5.4 Solution of Merton Problem: Examples -- 6.5.5 Solution of Merton Problem -- 6.6 Rates of Convergence in Averaging and Diffusion Approximations -- 6.7 Proofs -- 6.7.1 Proof of Theorem 6.1 -- 6.7.2 Proof of Theorem 6.2 -- 6.7.3 Proof of Theorem 6.3 -- 6.7.4 Proof of Proposition 6.1 -- 6.8 Concluding Remarks -- 7 Epidemic Models in Random Media -- 7.1 Introduction -- 7.2 From the Deterministic to Stochastic SARS Model -- 7.3 Averaging of Stochastic SARS Models -- 7.4 SARS Model in Merging Semi-Markov Random Media -- 7.5 Diffusion Approximation of Stochastic SARS Models in Semi-Markov Random Media -- 7.6 Concluding remarks -- 8 Optimal Stopping of Geometric Markov Renewal Chains and Pricing -- 8.1 Introduction -- 8.2 GMRC and Embedded Markov-Modulated (B,S)-Security Markets -- 8.2.1 Definition of the GMRC -- 8.2.2 Statement of the Problem: Optimal Stopping Rule -- 8.3 GMRP as Jump Discrete-Time Semi-Markov Random Evolution -- 8.4 Martingale Properties of GMRC -- 8.5 Optimal Stopping Rules for GMRC -- 8.6 Martingale Properties of Discount Price and Discount Capital -- 8.7 American Option Pricing Formulae for embedded Markov-modulated (B,S)-Security markets -- 8.8 European Option Pricing Formula for Embedded Markov-Modulated (B,S)-Security Markets -- 8.9 Proof of Theorems -- 8.10 Concluding Remarks -- A Markov Chains -- A.1 Transition Function -- A.2 Irreducible Markov Chains -- A.3 Recurrent Markov Chains -- A.4 Invariant Measures -- A.5 Uniformly Ergodic Markov Chains -- Bibliography -- Index.
Record Nr. UNINA-9910735778203321
Limnios Nikolaos  
Cham : , : Springer Nature Switzerland : , : Imprint : Birkhäuser, , 2023
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Local Limit Theorems for Inhomogeneous Markov Chains [[electronic resource] /] / by Dmitry Dolgopyat, Omri M. Sarig
Local Limit Theorems for Inhomogeneous Markov Chains [[electronic resource] /] / by Dmitry Dolgopyat, Omri M. Sarig
Autore Dolgopyat Dmitry
Edizione [1st ed. 2023.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023
Descrizione fisica 1 online resource (348 pages)
Disciplina 519.2
Altri autori (Persone) SarigOmri M
Collana Lecture Notes in Mathematics
Soggetto topico Probabilities
Stochastic processes
Dynamical systems
Probability Theory
Stochastic Processes
Dynamical Systems
Teoremes de límit (Teoria de probabilitats)
Processos de Markov
Soggetto genere / forma Llibres electrònics
ISBN 3-031-32601-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Acknowledgments -- Contents -- Notation -- 1 Overview -- 1.1 Setup and Aim -- 1.2 The Obstructions to the Local Limit Theorems -- 1.3 How to Show that the Obstructions Do Not Occur -- 1.4 What Happens When the Obstructions Do Occur -- 1.4.1 Lattice Case -- 1.4.2 Center-Tight Case -- 1.4.3 Reducible Case -- 1.5 Some Final Words on the Setup of this Work -- 1.6 Prerequisites -- 1.7 Notes and References -- 2 Markov Arrays, Additive Functionals, and Uniform Ellipticity -- 2.1 The Basic Setup -- 2.1.1 Inhomogeneous Markov Chains -- 2.1.2 Inhomogeneous Markov Arrays -- 2.1.3 Additive Functionals -- 2.2 Uniform Ellipticity -- 2.2.1 The Definition -- 2.2.2 Contraction Estimates and Exponential Mixing -- 2.2.3 Bridge Probabilities -- 2.3 Structure Constants -- 2.3.1 Hexagons -- 2.3.2 Balance and Structure Constants -- 2.3.3 The Ladder Process -- 2.4 γ-Step Ellipticity Conditions -- *2.5 Uniform Ellipticity and Strong Mixing Conditions -- 2.6 Reduction to Point Mass Initial Distributions -- 2.7 Notes and References -- 3 Variance Growth, Center-Tightness, and the CentralLimit Theorem -- 3.1 Main Results -- 3.1.1 Center-Tightness and Variance Growth -- 3.1.2 The Central Limit Theorem and theTwo-Series Theorem -- 3.2 Proofs -- 3.2.1 The Gradient Lemma -- 3.2.2 The Estimate of Var(SN) -- 3.2.3 McLeish's Martingale Central Limit Theorem -- 3.2.4 Proof of the Central Limit Theorem -- 3.2.5 Convergence of Moments -- 3.2.6 Characterization of Center-Tight Additive Functionals -- 3.2.7 Proof of the Two-Series Theorem -- *3.3 The Almost Sure Invariance Principle -- 3.4 Notes and References -- 4 The Essential Range and Irreducibility -- 4.1 Definitions and Motivation -- 4.2 Main Results -- 4.2.1 Markov Chains -- 4.2.2 Markov Arrays -- 4.2.3 Hereditary Arrays -- 4.3 Proofs -- 4.3.1 Reduction Lemma -- 4.3.2 Joint Reduction.
4.3.3 The Possible Values of the Co-Range -- 4.3.4 Calculation of the Essential Range -- 4.3.5 Existence of Irreducible Reductions -- 4.3.6 Characterization of Hereditary Additive Functionals -- 4.4 Notes and References -- 5 The Local Limit Theorem in the Irreducible Case -- 5.1 Main Results -- 5.1.1 Local Limit Theorems for Markov Chains -- 5.1.2 Local Limit Theorems for Markov Arrays -- 5.1.3 Mixing Local Limit Theorems -- 5.2 Proofs -- 5.2.1 Strategy of Proof -- 5.2.2 Characteristic Function Estimates -- 5.2.3 The LLT via Weak Convergence of Measures -- 5.2.4 The LLT in the Irreducible Non-Lattice Case -- 5.2.5 The LLT in the Irreducible Lattice Case -- 5.2.6 Mixing LLT -- 5.3 Notes and References -- 6 The Local Limit Theorem in the Reducible Case -- 6.1 Main Results -- 6.1.1 Heuristics and Warm Up Examples -- 6.1.2 The LLT in the Reducible Case -- 6.1.3 Irreducibility as a Necessary Condition for the Mixing LLT -- 6.1.4 Universal Bounds for Prob[SN-zN(a,b)] -- 6.2 Proofs -- 6.2.1 Characteristic Functions in the Reducible Case -- 6.2.2 Proof of the LLT in the Reducible Case -- 6.2.3 Necessity of the Irreducibility Assumption -- 6.2.4 Universal Bounds for Markov Chains -- 6.2.5 Universal Bounds for Markov Arrays -- 6.3 Notes and References -- 7 Local Limit Theorems for Moderate Deviationsand Large Deviations -- 7.1 Moderate Deviations and Large Deviations -- 7.2 Local Limit Theorems for Large Deviations -- 7.2.1 The Log Moment Generating Functions -- 7.2.2 The Rate Functions -- 7.2.3 The LLT for Moderate Deviations -- 7.2.4 The LLT for Large Deviations -- 7.3 Proofs -- 7.3.1 Strategy of Proof -- 7.3.2 A Parameterized Family of Changes of Measure -- 7.3.3 Choosing the Parameters -- 7.3.4 The Asymptotic Behavior of V"0365VξN(SN) -- 7.3.5 Asymptotics of the Log Moment Generating Functions -- 7.3.6 Asymptotics of the Rate Functions.
7.3.7 Proof of the Local Limit Theorem for Large Deviations -- 7.3.8 Rough Bounds in the Reducible Case -- 7.4 Large Deviations Thresholds -- 7.4.1 The Large Deviations Threshold Theorem -- 7.4.2 Admissible Sequences -- 7.4.3 Proof of the Large Deviations Threshold Theorem -- 7.4.4 Examples -- 7.5 Notes and References -- 8 Important Examples and Special Cases -- 8.1 Introduction -- 8.2 Sums of Independent Random Variables -- 8.3 Homogenous Markov Chains -- *8.4 One-Step Homogeneous Additive Functionals in L2 -- 8.5 Asymptotically Homogeneous Markov Chains -- 8.6 Equicontinuous Additive Functionals -- 8.7 Notes and References -- 9 Local Limit Theorems for Markov Chains in RandomEnvironments -- 9.1 Markov Chains in Random Environments -- 9.1.1 Formal Definitions -- 9.1.2 Examples -- 9.1.3 Conditions and Assumptions -- 9.2 Main Results -- 9.3 Proofs -- 9.3.1 Existence of Stationary Measures -- 9.3.2 The Essential Range is Almost Surely Constant -- 9.3.3 Variance Growth -- 9.3.4 Irreducibility and the LLT -- 9.3.5 LLT for Large Deviations -- 9.4 Notes and References -- A The Gärtner-Ellis Theorem in One Dimension -- A.1 The Statement -- A.2 Background from Convex Analysis -- A.3 Proof of the Gärtner-Ellis Theorem -- A.4 Notes and References -- B Hilbert's Projective Metric and Birkhoff's Theorem -- B.1 Hilbert's Projective Metric -- B.2 Contraction Properties -- B.3 Notes and References -- C Perturbations of Operators with Spectral Gap -- C.1 The Perturbation Theorem -- C.2 Some Facts from Analysis -- C.3 Proof of the Perturbation Theorem -- C.4 Notes and References -- References -- Index.
Record Nr. UNISA-996542671903316
Dolgopyat Dmitry  
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Local Limit Theorems for Inhomogeneous Markov Chains / / by Dmitry Dolgopyat, Omri M. Sarig
Local Limit Theorems for Inhomogeneous Markov Chains / / by Dmitry Dolgopyat, Omri M. Sarig
Autore Dolgopyat Dmitry
Edizione [1st ed. 2023.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023
Descrizione fisica 1 online resource (348 pages)
Disciplina 519.2
Altri autori (Persone) SarigOmri M
Collana Lecture Notes in Mathematics
Soggetto topico Probabilities
Stochastic processes
Dynamics
Probability Theory
Stochastic Processes
Dynamical Systems
Teoremes de límit (Teoria de probabilitats)
Processos de Markov
Soggetto genere / forma Llibres electrònics
ISBN 3-031-32601-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Acknowledgments -- Contents -- Notation -- 1 Overview -- 1.1 Setup and Aim -- 1.2 The Obstructions to the Local Limit Theorems -- 1.3 How to Show that the Obstructions Do Not Occur -- 1.4 What Happens When the Obstructions Do Occur -- 1.4.1 Lattice Case -- 1.4.2 Center-Tight Case -- 1.4.3 Reducible Case -- 1.5 Some Final Words on the Setup of this Work -- 1.6 Prerequisites -- 1.7 Notes and References -- 2 Markov Arrays, Additive Functionals, and Uniform Ellipticity -- 2.1 The Basic Setup -- 2.1.1 Inhomogeneous Markov Chains -- 2.1.2 Inhomogeneous Markov Arrays -- 2.1.3 Additive Functionals -- 2.2 Uniform Ellipticity -- 2.2.1 The Definition -- 2.2.2 Contraction Estimates and Exponential Mixing -- 2.2.3 Bridge Probabilities -- 2.3 Structure Constants -- 2.3.1 Hexagons -- 2.3.2 Balance and Structure Constants -- 2.3.3 The Ladder Process -- 2.4 γ-Step Ellipticity Conditions -- *2.5 Uniform Ellipticity and Strong Mixing Conditions -- 2.6 Reduction to Point Mass Initial Distributions -- 2.7 Notes and References -- 3 Variance Growth, Center-Tightness, and the CentralLimit Theorem -- 3.1 Main Results -- 3.1.1 Center-Tightness and Variance Growth -- 3.1.2 The Central Limit Theorem and theTwo-Series Theorem -- 3.2 Proofs -- 3.2.1 The Gradient Lemma -- 3.2.2 The Estimate of Var(SN) -- 3.2.3 McLeish's Martingale Central Limit Theorem -- 3.2.4 Proof of the Central Limit Theorem -- 3.2.5 Convergence of Moments -- 3.2.6 Characterization of Center-Tight Additive Functionals -- 3.2.7 Proof of the Two-Series Theorem -- *3.3 The Almost Sure Invariance Principle -- 3.4 Notes and References -- 4 The Essential Range and Irreducibility -- 4.1 Definitions and Motivation -- 4.2 Main Results -- 4.2.1 Markov Chains -- 4.2.2 Markov Arrays -- 4.2.3 Hereditary Arrays -- 4.3 Proofs -- 4.3.1 Reduction Lemma -- 4.3.2 Joint Reduction.
4.3.3 The Possible Values of the Co-Range -- 4.3.4 Calculation of the Essential Range -- 4.3.5 Existence of Irreducible Reductions -- 4.3.6 Characterization of Hereditary Additive Functionals -- 4.4 Notes and References -- 5 The Local Limit Theorem in the Irreducible Case -- 5.1 Main Results -- 5.1.1 Local Limit Theorems for Markov Chains -- 5.1.2 Local Limit Theorems for Markov Arrays -- 5.1.3 Mixing Local Limit Theorems -- 5.2 Proofs -- 5.2.1 Strategy of Proof -- 5.2.2 Characteristic Function Estimates -- 5.2.3 The LLT via Weak Convergence of Measures -- 5.2.4 The LLT in the Irreducible Non-Lattice Case -- 5.2.5 The LLT in the Irreducible Lattice Case -- 5.2.6 Mixing LLT -- 5.3 Notes and References -- 6 The Local Limit Theorem in the Reducible Case -- 6.1 Main Results -- 6.1.1 Heuristics and Warm Up Examples -- 6.1.2 The LLT in the Reducible Case -- 6.1.3 Irreducibility as a Necessary Condition for the Mixing LLT -- 6.1.4 Universal Bounds for Prob[SN-zN(a,b)] -- 6.2 Proofs -- 6.2.1 Characteristic Functions in the Reducible Case -- 6.2.2 Proof of the LLT in the Reducible Case -- 6.2.3 Necessity of the Irreducibility Assumption -- 6.2.4 Universal Bounds for Markov Chains -- 6.2.5 Universal Bounds for Markov Arrays -- 6.3 Notes and References -- 7 Local Limit Theorems for Moderate Deviationsand Large Deviations -- 7.1 Moderate Deviations and Large Deviations -- 7.2 Local Limit Theorems for Large Deviations -- 7.2.1 The Log Moment Generating Functions -- 7.2.2 The Rate Functions -- 7.2.3 The LLT for Moderate Deviations -- 7.2.4 The LLT for Large Deviations -- 7.3 Proofs -- 7.3.1 Strategy of Proof -- 7.3.2 A Parameterized Family of Changes of Measure -- 7.3.3 Choosing the Parameters -- 7.3.4 The Asymptotic Behavior of V"0365VξN(SN) -- 7.3.5 Asymptotics of the Log Moment Generating Functions -- 7.3.6 Asymptotics of the Rate Functions.
7.3.7 Proof of the Local Limit Theorem for Large Deviations -- 7.3.8 Rough Bounds in the Reducible Case -- 7.4 Large Deviations Thresholds -- 7.4.1 The Large Deviations Threshold Theorem -- 7.4.2 Admissible Sequences -- 7.4.3 Proof of the Large Deviations Threshold Theorem -- 7.4.4 Examples -- 7.5 Notes and References -- 8 Important Examples and Special Cases -- 8.1 Introduction -- 8.2 Sums of Independent Random Variables -- 8.3 Homogenous Markov Chains -- *8.4 One-Step Homogeneous Additive Functionals in L2 -- 8.5 Asymptotically Homogeneous Markov Chains -- 8.6 Equicontinuous Additive Functionals -- 8.7 Notes and References -- 9 Local Limit Theorems for Markov Chains in RandomEnvironments -- 9.1 Markov Chains in Random Environments -- 9.1.1 Formal Definitions -- 9.1.2 Examples -- 9.1.3 Conditions and Assumptions -- 9.2 Main Results -- 9.3 Proofs -- 9.3.1 Existence of Stationary Measures -- 9.3.2 The Essential Range is Almost Surely Constant -- 9.3.3 Variance Growth -- 9.3.4 Irreducibility and the LLT -- 9.3.5 LLT for Large Deviations -- 9.4 Notes and References -- A The Gärtner-Ellis Theorem in One Dimension -- A.1 The Statement -- A.2 Background from Convex Analysis -- A.3 Proof of the Gärtner-Ellis Theorem -- A.4 Notes and References -- B Hilbert's Projective Metric and Birkhoff's Theorem -- B.1 Hilbert's Projective Metric -- B.2 Contraction Properties -- B.3 Notes and References -- C Perturbations of Operators with Spectral Gap -- C.1 The Perturbation Theorem -- C.2 Some Facts from Analysis -- C.3 Proof of the Perturbation Theorem -- C.4 Notes and References -- References -- Index.
Record Nr. UNINA-9910736025503321
Dolgopyat Dmitry  
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Markov chains on metric spaces : a short course / / Michel Benaim, Tobias Hurth
Markov chains on metric spaces : a short course / / Michel Benaim, Tobias Hurth
Autore Benaim Michel
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , 2022
Descrizione fisica 1 online resource (205 pages)
Disciplina 519.233
Collana Universitext
Soggetto topico Markov processes
Metric spaces
Processos de Markov
Espais mètrics
Soggetto genere / forma Llibres electrònics
ISBN 3-031-11822-7
9783031118210
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Contents -- Preliminaries -- 1 Markov Chains -- 1.1 Markov Kernels -- 1.2 Markov Chains -- 1.3 The Canonical Chain -- 1.4 Markov and Strong Markov Properties -- 1.5 Continuous Time: Markov Processes -- 2 Countable Markov Chains -- 2.1 Recurrence and Transience -- 2.1.1 Positive Recurrence -- 2.1.2 Null Recurrence -- 2.2 Subsets of Recurrent Sets -- 2.3 Recurrence and Lyapunov Functions -- 2.4 Aperiodic Chains -- 2.5 The Convergence Theorem -- 2.6 Application to Renewal Theory -- 2.6.1 Coupling of Renewal Processes -- 2.7 Convergence Rates for Positive Recurrent Chains -- Notes -- 3 Random Dynamical Systems -- 3.1 General Definitions -- 3.2 Representation of Markov Chains by RDS -- Notes -- 4 Invariant and Ergodic Probability Measures -- 4.1 Weak Convergence of Probability Measures -- 4.1.1 Tightness and Prohorov's Theorem -- A Tightness Criterion -- 4.2 Invariant Measures -- 4.2.1 Tightness Criteria for Empirical Occupation Measures -- 4.3 Excessive Measures -- 4.4 Ergodic Measures -- 4.5 Unique Ergodicity -- 4.5.1 Unique Ergodicity of Random Contractions -- 4.6 Classical Results from Ergodic Theory -- 4.6.1 Poincaré, Birkhoff, and Ergodic Decomposition Theorems -- 4.7 Application to Markov Chains -- 4.8 Continuous Time: Invariant Probabilities for Markov Processes -- Notes -- 5 Irreducibility -- 5.1 Resolvent and ξ-Irreducibility -- 5.2 The Accessible Set -- 5.2.1 Continuous Time: Accessibility -- 5.3 The Asymptotic Strong Feller Property -- 5.3.1 Strong Feller Implies Asymptotic Strong Feller -- 5.3.2 A Sufficient Condition for the Asymptotic Strong Feller Property -- 5.3.3 Unique Ergodicity of Asymptotic Strong Feller Chains -- Notes -- 6 Petite Sets and Doeblin Points -- 6.1 Petite Sets, Small Sets, Doeblin Points -- 6.1.1 Continuous Time: Doeblin Points for Markov Processes -- 6.2 Random Dynamical Systems.
6.3 Random Switching Between Vector Fields -- 6.3.1 The Weak Bracket Condition -- 6.4 Piecewise Deterministic Markov Processes -- 6.4.1 Invariant Measures -- 6.4.2 The Strong Bracket Condition -- 6.5 Stochastic Differential Equations -- 6.5.1 Accessibility -- 6.5.2 Hörmander Conditions -- Notes -- 7 Harris and Positive Recurrence -- 7.1 Stability and Positive Recurrence -- 7.2 Harris Recurrence -- 7.2.1 Petite Sets and Harris Recurrence -- 7.3 Recurrence Criteria and Lyapunov Functions -- 7.4 Subsets of Recurrent Sets -- 7.5 Petite Sets and Positive Recurrence -- 7.6 Positive Recurrence for Feller Chains -- 7.6.1 Application to PDMPs -- 7.6.2 Application to SDEs -- 8 Harris Ergodic Theorem -- 8.1 Total Variation Distance -- 8.1.1 Coupling -- 8.2 Harris Convergence Theorems -- 8.2.1 Geometric Convergence -- Aperiodic Small Sets -- 8.2.2 Continuous Time: Exponential Convergence -- 8.2.3 Coupling, Splitting, and Polynomial Convergence -- 8.3 Convergence in Wasserstein Distance -- A Monotone Class and Martingales -- A.1 Monotone Class Theorem -- A.2 Conditional Expectation -- A.3 Martingales -- Bibliography -- List of Symbols -- List of Symbols -- Index.
Record Nr. UNINA-9910632475603321
Benaim Michel  
Cham, Switzerland : , : Springer, , 2022
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Markov chains on metric spaces : a short course / / Michel Benaim, Tobias Hurth
Markov chains on metric spaces : a short course / / Michel Benaim, Tobias Hurth
Autore Benaim Michel
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , 2022
Descrizione fisica 1 online resource (205 pages)
Disciplina 519.233
Collana Universitext
Soggetto topico Markov processes
Metric spaces
Processos de Markov
Espais mètrics
Soggetto genere / forma Llibres electrònics
ISBN 3-031-11822-7
9783031118210
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Contents -- Preliminaries -- 1 Markov Chains -- 1.1 Markov Kernels -- 1.2 Markov Chains -- 1.3 The Canonical Chain -- 1.4 Markov and Strong Markov Properties -- 1.5 Continuous Time: Markov Processes -- 2 Countable Markov Chains -- 2.1 Recurrence and Transience -- 2.1.1 Positive Recurrence -- 2.1.2 Null Recurrence -- 2.2 Subsets of Recurrent Sets -- 2.3 Recurrence and Lyapunov Functions -- 2.4 Aperiodic Chains -- 2.5 The Convergence Theorem -- 2.6 Application to Renewal Theory -- 2.6.1 Coupling of Renewal Processes -- 2.7 Convergence Rates for Positive Recurrent Chains -- Notes -- 3 Random Dynamical Systems -- 3.1 General Definitions -- 3.2 Representation of Markov Chains by RDS -- Notes -- 4 Invariant and Ergodic Probability Measures -- 4.1 Weak Convergence of Probability Measures -- 4.1.1 Tightness and Prohorov's Theorem -- A Tightness Criterion -- 4.2 Invariant Measures -- 4.2.1 Tightness Criteria for Empirical Occupation Measures -- 4.3 Excessive Measures -- 4.4 Ergodic Measures -- 4.5 Unique Ergodicity -- 4.5.1 Unique Ergodicity of Random Contractions -- 4.6 Classical Results from Ergodic Theory -- 4.6.1 Poincaré, Birkhoff, and Ergodic Decomposition Theorems -- 4.7 Application to Markov Chains -- 4.8 Continuous Time: Invariant Probabilities for Markov Processes -- Notes -- 5 Irreducibility -- 5.1 Resolvent and ξ-Irreducibility -- 5.2 The Accessible Set -- 5.2.1 Continuous Time: Accessibility -- 5.3 The Asymptotic Strong Feller Property -- 5.3.1 Strong Feller Implies Asymptotic Strong Feller -- 5.3.2 A Sufficient Condition for the Asymptotic Strong Feller Property -- 5.3.3 Unique Ergodicity of Asymptotic Strong Feller Chains -- Notes -- 6 Petite Sets and Doeblin Points -- 6.1 Petite Sets, Small Sets, Doeblin Points -- 6.1.1 Continuous Time: Doeblin Points for Markov Processes -- 6.2 Random Dynamical Systems.
6.3 Random Switching Between Vector Fields -- 6.3.1 The Weak Bracket Condition -- 6.4 Piecewise Deterministic Markov Processes -- 6.4.1 Invariant Measures -- 6.4.2 The Strong Bracket Condition -- 6.5 Stochastic Differential Equations -- 6.5.1 Accessibility -- 6.5.2 Hörmander Conditions -- Notes -- 7 Harris and Positive Recurrence -- 7.1 Stability and Positive Recurrence -- 7.2 Harris Recurrence -- 7.2.1 Petite Sets and Harris Recurrence -- 7.3 Recurrence Criteria and Lyapunov Functions -- 7.4 Subsets of Recurrent Sets -- 7.5 Petite Sets and Positive Recurrence -- 7.6 Positive Recurrence for Feller Chains -- 7.6.1 Application to PDMPs -- 7.6.2 Application to SDEs -- 8 Harris Ergodic Theorem -- 8.1 Total Variation Distance -- 8.1.1 Coupling -- 8.2 Harris Convergence Theorems -- 8.2.1 Geometric Convergence -- Aperiodic Small Sets -- 8.2.2 Continuous Time: Exponential Convergence -- 8.2.3 Coupling, Splitting, and Polynomial Convergence -- 8.3 Convergence in Wasserstein Distance -- A Monotone Class and Martingales -- A.1 Monotone Class Theorem -- A.2 Conditional Expectation -- A.3 Martingales -- Bibliography -- List of Symbols -- List of Symbols -- Index.
Record Nr. UNISA-996499868703316
Benaim Michel  
Cham, Switzerland : , : Springer, , 2022
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Markov processes and quantum theory / / Masao Nagasawa
Markov processes and quantum theory / / Masao Nagasawa
Autore Nagasawa Masao <1933 August 1->
Pubbl/distr/stampa Cham, Switzerland : , : Birkhäuser, , [2021]
Descrizione fisica 1 online resource (349 pages)
Disciplina 530.12
Collana Monographs in mathematics
Soggetto topico Quantum theory
Teoria quàntica
Processos de Markov
Soggetto genere / forma Llibres electrònics
ISBN 3-030-62688-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Contents -- Chapter 1 Mechanics of Random Motion -- 1.1 Smooth Motion and Random Motion -- 1.2 On Stochastic Processes -- 1.3 Itô's Path Analysis -- 1.4 Equation of Motion for a Stochastic Process -- 1.5 Kinematics of Random Motion -- 1.6 Free Random Motion of a Particle -- 1.7 Hooke's Force -- 1.8 Hooke's Force and an Additional Potential -- 1.9 Complex Evolution Functions -- 1.10 Superposition Principle -- 1.11 Entangled Quantum Bit -- 1.12 Light Emission from a Silicon Semiconductor -- 1.13 The Double-Slit Problem -- 1.14 Double-Slit Experiment with Photons -- 1.15 Theory of Photons -- 1.16 Principle of Least Action -- 1.17 Transformation of Probability Measures -- 1.18 Schrödinger Equation and Path Equation -- Chapter 2 Applications -- 2.1 Motion induced by the Coulomb Potential -- 2.2 Charged Particle in a Magnetic Field -- 2.3 Aharonov-Bohm Effect -- 2.4 Tunnel Effect -- 2.5 Bose-Einstein Distribution -- 2.6 Random Motion and the Light Cone -- 2.7 Origin of the Universe -- 2.8 Classification of Boundary Points -- 2.9 Particle Theory of Electron Holography -- 2.10 Escherichia coli and Meson models -- 2.11 High-Temperature Superconductivity -- Chapter 3 Momentum, Kinetic Energy, Locality -- 3.1 Momentum and Kinetic Energy -- 3.2 Matrix Mechanics -- 3.3 Function Representations of Operators -- 3.4 Expectation and Variance -- 3.5 The Heisenberg Uncertainty Principle -- 3.6 Kinetic Energy and Variance of Position -- 3.7 Theory of Hidden Variables -- 3.8 Einstein's Locality -- 3.9 Bell's Inequality -- 3.10 Local Spin Correlation Model -- 3.11 Long-Lasting Controversy and Random Motion -- Chapter 4 Markov Processes -- 4.1 Time-Homogeneous Markov Proces-ses -- 4.2 Transformations by M-Functionals -- 4.3 Change of Time Scale -- 4.4 Duality and Time Reversal -- 4.5 Time Reversal, Last Occurrence Time.
4.6 Time Reversal, Equations of Motion -- 4.7 Conditional Expectation -- 4.8 Paths of Brownian Motion -- Chapter 5 Applications of Relative Entropy -- 5.1 Relative Entropy -- 5.2 Variational Principle -- 5.3 Exponential Family of Distributions -- 5.4 Existence of Entrance and Exit Functions -- 5.5 Cloud of Paths -- 5.6 Kac's Phenomenon of Propagation of Chaos -- Chapter 6 Extinction and Creation -- 6.1 Extinction of Particles -- 6.2 Piecing-Together Markov Processes -- 6.3 Branching Markov Processes -- 6.4 Construction of Branching Markov Processes -- 6.5 Markov Processes with Age -- 6.6 Branching Markov Processes with Age -- Bibliography -- Index.
Record Nr. UNISA-996466405103316
Nagasawa Masao <1933 August 1->  
Cham, Switzerland : , : Birkhäuser, , [2021]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Markov processes and quantum theory / / Masao Nagasawa
Markov processes and quantum theory / / Masao Nagasawa
Autore Nagasawa Masao <1933 August 1->
Pubbl/distr/stampa Cham, Switzerland : , : Birkhäuser, , [2021]
Descrizione fisica 1 online resource (349 pages)
Disciplina 530.12
Collana Monographs in mathematics
Soggetto topico Quantum theory
Teoria quàntica
Processos de Markov
Soggetto genere / forma Llibres electrònics
ISBN 3-030-62688-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Contents -- Chapter 1 Mechanics of Random Motion -- 1.1 Smooth Motion and Random Motion -- 1.2 On Stochastic Processes -- 1.3 Itô's Path Analysis -- 1.4 Equation of Motion for a Stochastic Process -- 1.5 Kinematics of Random Motion -- 1.6 Free Random Motion of a Particle -- 1.7 Hooke's Force -- 1.8 Hooke's Force and an Additional Potential -- 1.9 Complex Evolution Functions -- 1.10 Superposition Principle -- 1.11 Entangled Quantum Bit -- 1.12 Light Emission from a Silicon Semiconductor -- 1.13 The Double-Slit Problem -- 1.14 Double-Slit Experiment with Photons -- 1.15 Theory of Photons -- 1.16 Principle of Least Action -- 1.17 Transformation of Probability Measures -- 1.18 Schrödinger Equation and Path Equation -- Chapter 2 Applications -- 2.1 Motion induced by the Coulomb Potential -- 2.2 Charged Particle in a Magnetic Field -- 2.3 Aharonov-Bohm Effect -- 2.4 Tunnel Effect -- 2.5 Bose-Einstein Distribution -- 2.6 Random Motion and the Light Cone -- 2.7 Origin of the Universe -- 2.8 Classification of Boundary Points -- 2.9 Particle Theory of Electron Holography -- 2.10 Escherichia coli and Meson models -- 2.11 High-Temperature Superconductivity -- Chapter 3 Momentum, Kinetic Energy, Locality -- 3.1 Momentum and Kinetic Energy -- 3.2 Matrix Mechanics -- 3.3 Function Representations of Operators -- 3.4 Expectation and Variance -- 3.5 The Heisenberg Uncertainty Principle -- 3.6 Kinetic Energy and Variance of Position -- 3.7 Theory of Hidden Variables -- 3.8 Einstein's Locality -- 3.9 Bell's Inequality -- 3.10 Local Spin Correlation Model -- 3.11 Long-Lasting Controversy and Random Motion -- Chapter 4 Markov Processes -- 4.1 Time-Homogeneous Markov Proces-ses -- 4.2 Transformations by M-Functionals -- 4.3 Change of Time Scale -- 4.4 Duality and Time Reversal -- 4.5 Time Reversal, Last Occurrence Time.
4.6 Time Reversal, Equations of Motion -- 4.7 Conditional Expectation -- 4.8 Paths of Brownian Motion -- Chapter 5 Applications of Relative Entropy -- 5.1 Relative Entropy -- 5.2 Variational Principle -- 5.3 Exponential Family of Distributions -- 5.4 Existence of Entrance and Exit Functions -- 5.5 Cloud of Paths -- 5.6 Kac's Phenomenon of Propagation of Chaos -- Chapter 6 Extinction and Creation -- 6.1 Extinction of Particles -- 6.2 Piecing-Together Markov Processes -- 6.3 Branching Markov Processes -- 6.4 Construction of Branching Markov Processes -- 6.5 Markov Processes with Age -- 6.6 Branching Markov Processes with Age -- Bibliography -- Index.
Record Nr. UNINA-9910488723503321
Nagasawa Masao <1933 August 1->  
Cham, Switzerland : , : Birkhäuser, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Markov Renewal and Piecewise Deterministic Processes [[electronic resource] /] / by Christiane Cocozza-Thivent
Markov Renewal and Piecewise Deterministic Processes [[electronic resource] /] / by Christiane Cocozza-Thivent
Autore Cocozza-Thivent Christiane
Edizione [1st ed. 2021.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021
Descrizione fisica 1 online resource (XIV, 252 p. 16 illus., 4 illus. in color.)
Disciplina 519.233
Collana Probability Theory and Stochastic Modelling
Soggetto topico Markov processes
Computer science - Mathematics
Mathematical statistics
Markov Process
Probability and Statistics in Computer Science
Processos de Markov
Estadística matemàtica
Soggetto genere / forma Llibres electrònics
ISBN 3-030-70447-5
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Tools -- Markov renewal processes and related processes -- First steps with PDMP -- Hitting time distribution -- Intensity of some marked point pocesses -- Generalized Kolmogorov equations -- A martingale approach -- Stability -- Numerical methods -- Switching Processes -- Tools -- Interarrival distribution with several Dirac measures -- Algorithm convergence's proof.
Record Nr. UNISA-996466393303316
Cocozza-Thivent Christiane  
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Markov Renewal and Piecewise Deterministic Processes / / by Christiane Cocozza-Thivent
Markov Renewal and Piecewise Deterministic Processes / / by Christiane Cocozza-Thivent
Autore Cocozza-Thivent Christiane
Edizione [1st ed. 2021.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021
Descrizione fisica 1 online resource (XIV, 252 p. 16 illus., 4 illus. in color.)
Disciplina 519.233
Collana Probability Theory and Stochastic Modelling
Soggetto topico Markov processes
Computer science - Mathematics
Mathematical statistics
Markov Process
Probability and Statistics in Computer Science
Processos de Markov
Estadística matemàtica
Soggetto genere / forma Llibres electrònics
ISBN 3-030-70447-5
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Tools -- Markov renewal processes and related processes -- First steps with PDMP -- Hitting time distribution -- Intensity of some marked point pocesses -- Generalized Kolmogorov equations -- A martingale approach -- Stability -- Numerical methods -- Switching Processes -- Tools -- Interarrival distribution with several Dirac measures -- Algorithm convergence's proof.
Record Nr. UNINA-9910484004403321
Cocozza-Thivent Christiane  
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
MCMC from scratch : a practical introduction to Markov Chain Monte Carlo / / Masanori Hanada and So Matsuura
MCMC from scratch : a practical introduction to Markov Chain Monte Carlo / / Masanori Hanada and So Matsuura
Autore Hanada Masanori
Pubbl/distr/stampa Singapore : , : Springer, , [2022]
Descrizione fisica 1 online resource (198 pages)
Disciplina 530.12
Soggetto topico Markov processes
Processos de Markov
Soggetto genere / forma Llibres electrònics
ISBN 981-19-2715-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910619274803321
Hanada Masanori  
Singapore : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui