MATH-332: Markov chains

Lectures in this course (32)

Explores exponential random variables, memorylessness, and applications in stochastic processes and Markov chains.

Covers supporting hyperplanes for approximating functions' graphs in different dimensions using hyperplanes and linear approximations.

Explores the properties of invariant distributions in Markov chains and provides examples of their application.

Explores positive recurrence and invariant distributions in Markov chains, discussing their relationship and implications.

Explores invariant distributions for Markov Chains, emphasizing uniqueness and implications in communicating classes.

Covers various examples related to recurrence, including mathematical formulas and calculations.

Explores recurrence, transience, and invariant distributions in Markov chains with proofs and examples.

Covers the proof of the equivalence between AQ=0 and XP(s)=X.

Offers a review of key probability concepts, including probability spaces, events, and random variables.

Covers the review of probability tools, including independence, conditional probability, and random variables.

Explores the notions of transience and recurrence in Markov chains through various examples and calculations.

Explores transience and recurrence in Markov chains, emphasizing birth and death chains and random walks.

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