Discrete-Time Markov Chains: Definitions

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Related lectures (32)

Markov Chains: Ergodicity and Stationary Distribution

Explores ergodicity and stationary distribution in Markov chains, emphasizing convergence properties and unique distributions.

Markov Chains: Challenges and Surprises

Explores challenges and surprises in Markov chains, analyzing convergence, state grouping, and transition probabilities.

Markov Chains: Properties and Expectations

Explores Markov chains' properties, expectations, and recurrence in Poisson processes.

Probability Inequalities

Explores probability inequalities, convergence types, and moment generating functions for distribution approximation.

Discrete-Time Markov Chains: Absorbing Chains Examples

Explores examples of absorbing chains in discrete-time Markov chains, focusing on transition probabilities.

Probability and Statistics

Delves into probability, statistics, paradoxes, and random variables, showcasing their real-world applications and properties.

Markov Chains: Stationary Distributions

Explores Markov chains and stationary distributions, emphasizing the importance of identifying them for improving convergence.

Markov Chain Games

Explores Markov chain games, hitting probabilities, and expected hitting times in a target set.

Markov Chain Analysis

Delves into Markov chains by analyzing a scenario with two fleas moving in opposite directions, exploring transition matrices and probabilities over time.

Introduction to Convexity

Introduces the key concepts of convexity and its applications in different fields.

Hitting Times: Examples

Explores hitting times in Markov chains through various examples and scenarios.

Markov Chains: Recurrence and Transience

Explores first passage times, strong Markov property, and state recurrence/transience in Markov chains.

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