Markov chains

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Related lectures (32)

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Stochastic Processes: Markov Chains

Covers stochastic processes, focusing on Markov chains and their applications in real-world scenarios.

Markov Chains: Applications and Sampling Methods

Covers the basics of Markov chains and their algorithmic applications.

MCMC Examples and Error Estimation

Covers Markov Chain Monte Carlo examples and error estimation methods.

Applied Probability & Stochastic Processes

Covers applied probability, Markov chains, and stochastic processes, including transition matrices, eigenvalues, and communication classes.

Stationary Distribution in Markov Chains

Explores the concept of stationary distribution in Markov chains, discussing its properties and implications, as well as the conditions for positive-recurrence.

Bonus Malus System: Transition Probabilities

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

Quantum Monte Carlo: Basic Concepts

Covers the basic concepts of Quantum Monte Carlo and its application in spin chains, Markov chains, and Monte Carlo simulations.

Markov Chain Monte Carlo: Rejection Sampling

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

Markov Chains: Theory and Applications

Covers the theory and applications of Markov chains in modeling random phenomena and decision-making under uncertainty.

Continuous Random Variables

Covers continuous random variables, probability density functions, and distributions, with practical examples.