Bayesian inference

Related lectures (32)

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

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

Covers the general concepts of Markov chains and their applications in various fields.

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

Explores quantum entropy in Markov chains and Bell states, emphasizing entanglement.

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

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

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

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

Covers the theory of Markov chains and Monte Carlo methods.