Markov Chain Analysis

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Related lectures (32)

Page 1 of 4

Probability & Stochastic Processes

Covers applied probability, stochastic processes, Markov chains, rejection sampling, and Bayesian inference methods.

Markov Chains: Introduction and Properties

Covers the introduction and properties of Markov chains, including transition matrices and stochastic processes.

Markov Chains: Hitting Probabilities

Explores hitting probabilities in Markov chains, covering minimal solutions, proofs, and recursive relationships.

Markov Chains: Theory and Applications

Covers the theory and applications of Markov chains, focusing on key concepts and properties.

Markov Chain Monte Carlo: Rejection Sampling

Explores rejection sampling for generating sample values from a target distribution, along with Bayesian inference using MCMC.

Invariant Measures: Properties and Applications

Covers the concept of invariant measures in Markov chains and their role in analyzing irreducible recurrent processes.

Bonus Malus System: Transition Probabilities

Explores the Bonus Malus system for insurance premiums and Markov chain transition probabilities.

Markov Chains: Transition Densities

Covers Markov processes, transition densities, and distribution conditional on information, discussing classification of states and stationary distributions.

Propagation of Uncertainty: Estimation and Distribution

Discusses estimation and propagation of uncertainty in random variables and the importance of managing uncertainty in statistical analysis.

Markov chains

Covers Markov chains, Monte Carlo sampling, isotropy, and the curse of dimensionality.