LEADER 08704nam 22006735 450 001 9910736025503321 005 20240522135815.0 010 $a3-031-32601-6 024 7 $a10.1007/978-3-031-32601-1 035 $a(MiAaPQ)EBC30670618 035 $a(Au-PeEL)EBL30670618 035 $a(DE-He213)978-3-031-32601-1 035 $a(PPN)272249351 035 $a(CKB)27899953800041 035 $a(EXLCZ)9927899953800041 100 $a20230731d2023 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aLocal Limit Theorems for Inhomogeneous Markov Chains$b[electronic resource] /$fby Dmitry Dolgopyat, Omri M. Sarig 205 $a1st ed. 2023. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2023. 215 $a1 online resource (348 pages) 225 1 $aLecture Notes in Mathematics,$x1617-9692 ;$v2331 311 08$aPrint version: Dolgopyat, Dmitry Local Limit Theorems for Inhomogeneous Markov Chains Cham : Springer International Publishing AG,c2023 9783031326004 327 $aIntro -- 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 ?-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. 327 $a4.3.3 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"0365V?N(SN) -- 7.3.5 Asymptotics of the Log Moment Generating Functions -- 7.3.6 Asymptotics of the Rate Functions. 327 $a7.3.7 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. 330 $aThis book extends the local central limit theorem to inhomogeneous Markov chains whose state spaces and transition probabilities are allowed to change in time. Such chains are used to model Markovian systems depending on external time-dependent parameters. It develops a new general theory of local limit theorems for additive functionals of Markov chains, in the regimes of local, moderate, and large deviations, and provides nearly optimal conditions for the classical expansions, as well as asymptotic corrections when these conditions fail. Applications include local limit theorems for independent but not identically distributed random variables, Markov chains in random environments, and time-dependent perturbations of homogeneous Markov chains. The inclusion of numerous examples, a comprehensive review of the literature, and an account of the historical background of the subject make this self-contained book accessible to graduate students. It will also be useful for researchers in probability and ergodic theory who are interested in asymptotic behaviors, random walks in random environments, random dynamical systems and non-stationary systems. 410 0$aLecture Notes in Mathematics,$x1617-9692 ;$v2331 606 $aProbabilities 606 $aStochastic processes 606 $aDynamical systems 606 $aProbability Theory 606 $aStochastic Processes 606 $aDynamical Systems 606 $aTeoremes de límit (Teoria de probabilitats)$2thub 606 $aProcessos de Markov$2thub 608 $aLlibres electrònics$2thub 615 0$aProbabilities. 615 0$aStochastic processes. 615 0$aDynamical systems. 615 14$aProbability Theory. 615 24$aStochastic Processes. 615 24$aDynamical Systems. 615 7$aTeoremes de límit (Teoria de probabilitats) 615 7$aProcessos de Markov 676 $a519.2 700 $aDolgopyat$b Dmitry$01379869 701 $aSarig$b Omri M$01379870 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910736025503321 996 $aLocal Limit Theorems for Inhomogeneous Markov Chains$93420274 997 $aUNINA