Category
Bayesian inference
Related lectures (257)
Markov Chains and Algorithm Applications
Covers Markov chains and their applications in algorithms, focusing on Markov Chain Monte Carlo sampling and the Metropolis-Hastings algorithm.
Markov chains
Covers Markov chains, Monte Carlo sampling, isotropy, and the curse of dimensionality.
MCMC Examples and Error Estimation
Covers Markov Chain Monte Carlo examples and error estimation methods.
Markov Chains: Applications and Sampling Methods
Covers the basics of Markov chains and their algorithmic applications.
Markov Chains and Algorithm Applications
Covers the application of Markov chains and algorithms for function optimization and graph colorings.
Markov Chains and Algorithm Applications
Explores Markov chains and algorithm applications, including exact simulation and Propp-Wilson algorithms.
Markov Chains: Communicating Classes
Explores communicating classes in Markov chains, distinguishing between transient and recurrent classes, and delves into the properties of these classes.
Probability & Stochastic Processes
Covers applied probability, stochastic processes, Markov chains, rejection sampling, and Bayesian inference methods.
Markov Chains: Simulation and Optimization
Explores Markov chains, Metropolis-Hastings, and simulation for optimization purposes, highlighting the significance of ergodicity in efficient variable simulation.
Markov Chains: Applications and Coupled Chains
Covers Markov chains, coupled chains, and their applications, emphasizing the importance of irreducibility.