Venue: TBA
Class Timings: TBA
First Meeting: TBA
Course Description:
- Discrete time Markov chains: for countable state space, classification of states
- Discrete parameter martingales: conditional expectation, optional sampling theorems, Doob’s inequalities, martingale convergence theorems
- Brownian motion: construction, continuity properties, Markov and strong Markov property and applications, Donsker’s invariance principle, sample path properties
Course Outcomes:
- Develop a thorough understanding of Markov chains, martingales, and Brownian motion
- Apply key theorems in stochastic processes to analyze random systems
- Utilize probabilistic models to solve real-world problems involving uncertainty and randomness
- Teacher: Riddhipratim Basu
Credit Score: 4