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 and
Week 12 : Introduction to Continuous Markov Chains
DOWNLOAD APP
FOLLOW US