Probability Stochastic Process (V-U)

Probability Experiments: Repeated, Dependent, Cascaded (V-U)

Equally likely outcomes, Random Variables, Chuk-a-luck, Binomial & Poisson Distributions (V-U)

Random walk on real line, Random walk on 2-d plane, Random walk in higher dimensions, Continuous-time Markov chains (V-U)

Expectation of Transformed Random Variables, Covariance Matrix, Eigenvalues & Eigenvectors (V-U)

Relations, Functions, Probability (V-U)

Exponential RV Pair, Pair of Dependent RVs, Correlation & Covariance (V-U)

Scaling of Gaussian RV, Standard Gaussian Events, Joint Distributions, Pair of RVs (V-U)

Failure of TV Set Example, Gaussian RV (V-U)

Set Theory (V-U)

Function of two RVs, Evaluation of Fz(Z), CDF & PDF of Z = X + Y (V-U)

Trains on a Junction, A local bus stop, Random line partition (V-U)

Function of two RVs, Evaluation of Fz(Z), CDF & PDF of Z = Y / X, CDF & PDF of Z = max (X, Y) (V-U)

Transformation of RVs, Conditional Expectation, Co-variance and Correlation (V-U)

Introduction to Probability and Stochastic Processes (V-U)

Transient and recurrent Markov chains, Return times of recurrent states (V-U)

Joint Probability, Conditional Probability, Total Probability (V-U)

Transition probabilities, Stochastic matrix, Stationary probability distribution (V-U)

Markov chains with infinite state space (V-U)

Vector RVs, Transforming Vector RVs (V-U)

Mean of Transformed RV, Height Distribution of Humans Example, Higher Moments (V-U)