Markov chain

Formal sciences
Statistics
Statistical inference
Bayesian inference

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Invariant Distributions: Markov Chains

Explores invariant distributions, recurrent states, and convergence in Markov chains, including practical applications like PageRank in Google.

Markov Chains: Applications and Sampling Methods

Covers the basics of Markov chains and their algorithmic applications.

Markov chains

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

Markov Chains: Ergodic Chains Examples

Covers stochastic models for communications, focusing on discrete-time Markov chains.

Ergodic Theorem: Proof and Applications

Explains the proof of the ergodic theorem and the concept of positive-recurrence in Markov chains.

Lindblad equation

Covers the derivation of the Lindblad equation and the evolution of quantum gases.

Positive recurrence: invariant distributions

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

Probability & Stochastic Processes

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

Theory of MCMC

Covers the theory of Markov Chain Monte Carlo (MCMC) sampling and discusses convergence conditions, transition matrix choice, and target distribution evolution.

Recurrence and transience in markov chains

Explores the concepts of recurrence and transience in continuous time Markov chains.