INTRODUCTION TO STOCHASTIC PROCESSES describes the main features of major stochastic processes, giving definition of basic concepts and presenting key results with rigorous proofs. The treatment is lucid and easy to follow. The theory is developed from basic foundation with a view to build a sound understanding of the subject. An introduction to ergodic theory is presented in the second part.

• • Numerous examples and applications. • Discussions on more advanced topics in the last section of each chapter. • Treatment of various lesser known processes like demi-martingales, quasimartingales. • Discussions on mixing time of Markov chains, Poisson and Martin boundary. • An up-to-date treatment of U-statistics. • Basic notions of stochastic integration, martingale problem and Girsanov theorem. • A treatment of ergodic theorems, entropy and relation to information theory.

Stochastic Processes / Conditional Expectation / Weak Convergence / Discrete-Time Markov Chains / Continuous Time Markov Chains / Markov Processes / Discrete Parameter Martingale / Continuous Parameter Martingale / Poisson Process / Brownian Motion / Levy Process / Stochastic Calculus / Ergodic Theory.

Postgraduate Students