Renewal theory

Abstract: * Measure Theory-Basic Notions * Measure Theory-Key Results * Processes, Distributions, and Independence * Random Sequences, Series, and Averages * Characteristic Functions and Classical Limit Theorems * Conditioning and Disintegration * Martingales and Optional Times * Markov Processes and Discrete-Time Chains * Random Walks and Renewal Theory * Stationary Processes and Ergodic Theory * Special Notions of Symmetry and Invariance * Poisson and Pure Jump-Type Markov Processes * Gaussian Processes and Brownian Motion * Skorohod Embedding and Invariance Principles * Independent Increments and Infinite Divisibility * Convergence of Random Processes, Measures, and Sets * Stochastic Integrals and Quadratic Variation * Continuous Martingales and Brownian Motion * Feller Processes and Semigroups * Ergodic Properties of Markov Processes * Stochastic Differential Equations and Martingale Problems * Local Time, Excursions, and Additive Functionals * One-Dimensional SDEs and Diffusions * Connections with PDEs and Potential Theory * Predictability, Compensation, and Excessive Functions * Semimartingales and General Stochastic Integration * Large Deviations * Appendix 1: Advanced Measure Theory * Appendix 2: Some Special Spaces * Historical and Bibliographical Notes * Bibliography * Indices

Abstract: Preface SIMPLE MARKOVIAN MODELS: Markov Chains Markov Jump Processes Queueing Theory at the Markovian Level BASIC MATHEMATICAL TOOLS: Basic Renewal Theory Regenerative Processes Further Topics in Renewal Theory and Regenerative Processes Random Walks SPECIAL MODELS AND METHODS: Steady-state Properties of GI/G/1 Explicit Examples in the Theory of Random Walks and Single Server Queues Multi-Dimensional Methods Many-server Queues Conjugate Processes Insurance Risk, Dam and Storage Models Selected Background and Notation.

Abstract: Stochastic Modeling. Probability Review. The Major Discrete Distributions. @rtant Continuous Distributions. Some Elementary Exercises. Useful Functions, Integrals, and Sums. Conditional Probability and Conditional Expectation: The Discrete Case. The Dice Game Craps. Random Sums. Conditioning on a Continuous Random Variable. Markov Chains: Introduction: Definitions. Transition Probability Matrices of a Markov Chain. Some Markov Chain Models. First Step Analysis. Some Special Markov Chains. Functionals of Random Walks and Success Runs. Another Look at First Step Analysis. The Long Run Behavior of Markov Chains: Regular Transition Probability Matrices. Examples. The Classification of States. The Basic Limit Theorem of Markov Chains. Reducible Markov Chains. Sequential Decisions and Markov Chains. Poisson Processes: The Poisson Distribution and the Poisson Processes. The Law of Rare Events. Distributions Associated with the Poisson Process. The Uniform Distribution and Poisson Processes. Spatial Poisson Processes. Compound and Marked Poisson Processes. Continuous Time Markov Chains: Pure Birth Processes. Ptire Death Processes. Birth and Death Processes. The Limiting Behavior of Birth and Death Processes. Birth and Death Processes with Absorbing States. Finite State Continuous Time Markov Chains. Set Valued Processes. Renewal Phenomena: Definition of a Renewal Process and Related Concepts. Some Examples of Renewal Processes. The Poisson Process Viewed as a Renewal Process. The Asymptotic ]3ehavior as Renewal Process.

Abstract: Two types of preventive maintenance policies are considered. A policy is defined to be optimum if it maximizes “limiting efficiency,” i.e., fractional amount of up-time over long intervals. Elementary renewal theory is used to obtain optimum policies. The optimum policies are determined, in each case, as unique solutions of certain integral equations depending on the failure distribution. It is shown that both solutions are also minimum cost solutions when the proper identifications are made. The two optimum policies are compared under certain restrictions.

Abstract: where Mn and Qn are random d • d matrices respectively d-vectors and Yn also is a d-vector. Throughout we take the sequence of pairs (Mn, Q~), n >/1, independently and identically distributed. The equation (1.1) arises in various contexts. We first met a special case in a paper by Solomon, [20] sect. 4, which studies random walks in random environments. Closely related is the fact tha t if Yn(i) is the expected number of particles of type i in the nth generation of a d-type branching process in a random environment with immigration, then Yn = (Yn(1) ..... Yn(d)) satisfies (1.1) (Qn represents the immigrants in the nth generation). (1.1) has been used for the amount of radioactive material in a compar tment ([17]) and in control theory [9 a]. Moreover, it is the principal feacture in a model for evolution and cultural inheritance by Cavalli-Sforza and Feldman [2]. Notice also tha t the dth order linear difference equation