Stationary Processes and Discrete Parameter Markov Processes

Rabi Bhattacharya is Professor of Mathematics at The University of Arizona. He is a Fellow of the Institute of Mathematical Statistics and a recipient of the U.S. Senior Scientist Humboldt Award and of a Guggenheim Fellowship. He has made significant contributions to the theory and application of Markov processes, and more recently, nonparametric statistical inference on manifolds. He has served on editorial boards of many international journals and has published several research monographs and graduate texts on probability and statistics.Edward C. Waymire is Emeritus Professor of Mathematics at Oregon State University. He received a PhD in mathematics from the University of Arizona in the theory of interacting particle systems. His primary research concerns applications of probability and stochastic processes to problems of contemporary applied mathematics pertaining to various types of flows, dispersion, and random disorder. He is a former chief editor of the Annals of Applied Probability, and past president of the Bernoulli Society for Mathematical Statistics and Probability.Both authors have co-authored numerous books, including A Basic Course in Probability Theory, which is an ideal companion to the current volume.

"The book is an advanced level measure theoretic probability book. ... The book is an impressive presentation of material, including a huge variety of topics in probability. Because of the wealth of subjects, there is an abundance of possible research topics waiting to challenge new (or experienced) probability experts. This book would be an excellent text for an advanced probability course, and is certainly a valuable reference for those interested in the exciting field of probability." (Myron Hlynka, Mathematical Reviews, March, 2025)

• Symbol Definition List.- 1. Fourier Analysis: A Brief.- 2. Weakly Stationary Processes and their Spectral Measures.- 3. Spectral Representation of Stationary Processes.- 4. Birkhoff’s Ergodic Theorem.- 5. Subadditive Ergodic Theory.- 6. An Introduction to Dynamical Systems.- 7. Markov Chains.- 8. Markov Processes with General State Space.- 9. Stopping Times and the Strong Markov Property.- 10. Transience and Recurrence of Markov Chains.- 11. Birth–Death Chains.- 12. Hitting Probabilities & Absorption.- 13. Law of Large Numbers and Invariant Probability for Markov Chains by Renewal Decomposition.- 14. The Central Limit Theorem for Markov Chains by Renewal Decomposition.- 15. Martingale Central Limit Theorem.- 16. Stationary Ergodic Markov Processes: SLLN & FCLT.- 17. Linear Markov Processes.- 18. Markov Processes Generated by Iterations of I.I.D. Maps.- 19. A Splitting Condition and Geometric Rates of Convergence to Equilibrium.- 20. Irreducibility and Harris Recurrent Markov Processes.- 21. An Extended Perron–Frobenius Theorem and Large Deviation Theory for Markov Processes.- 22. Special Topic: Applications of Large Deviation Theory.- 23. Special Topic: Associated Random Fields, Positive Dependence, FKG Inequalities.- 24. Special Topic: More on Coupling Methods and Applications.- 25. Special Topic: An Introduction to Kalman Filter.- A. Spectral Theorem for Compact Self-Adjoint Operators and Mercer’s Theorem.- B. Spectral Theorem for Bounded Self-Adjoint Operators.- C. Borel Equivalence for Polish Spaces.- D. Hahn–Banach, Separation, and Representation Theorems in Functional Analysis.- References.- Author Index.- Subject Index.