Introduction to Stochastic Processes free videos and free material uploaded by IIT Bombay Staff .
Week 1 : Introductions to events, probability, conditional probability, Bayes rule
Week 2 : Random Varaibles, Expectations, Variance, Various type of distributions
Week 3 : CDF and PDF of random variables. Conditional CDF and PDFs
Week 4 : Jointly distributed random variables, covariance and independence
Week 5 : Transformation of random variables and their distributions
Week 6 : Introductions to Random processes. Stationary and Ergodicity
Week 7 : Convergence of Sequence of RVs. (almost surely, in probability, in distributions).
Week 8 : Strong and weak law of large numbers, central limit theorem
Week 9 : Discrete Markov chains. Stopping time and Strong Markov property Classification of Transient and Recurrent states
Week 10 : Counting Process, Poisson Processes and its applications
Week 11 : Renewal Theory. Elementary and Renewal Reward Theorem andWeek 12 : Introduction to Continuous Markov Chains
Randomness is a common thing that we came across in our daily life. Questions like how much traffic will be on my route today? how much I need to wait to catch a bus to my workplace? will I gain or lose money in stock market? may not have fixed answers as they are associated with events that are not in our control and could be treated as random. In this course, we will learn various probability techniques to model random events and study how to analyze their effect.
INTENDED AUDIENCE : All disciplines learners
PRE-REQUISITE : Introductory real analysis
SUPPORT INDUSTRY : This is a basic course. All companies will recognize
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