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Lecture
MCMC Examples and Error Estimation
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Related lectures (32)
Theory of MCMC
Covers the theory of Markov Chain Monte Carlo (MCMC) sampling and discusses convergence conditions, transition matrix choice, and target distribution evolution.
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.
Modeling Neurobiological Signals: Markov Chains
Explores modeling neurobiological signals with Markov Chains, focusing on parameter estimation and data classification.
Markov Chains: Applications and Sampling Methods
Covers the basics of Markov chains and their algorithmic applications.
Monte Carlo Markov Chains
Covers the theory of Markov chains and Monte Carlo methods.
Markov Chains: Applications and Analysis
Explores Markov chains, focusing on the coloring problem and algorithm analysis.
Probability & Stochastic Processes
Covers applied probability, stochastic processes, Markov chains, rejection sampling, and Bayesian inference methods.
Hidden Markov Models (HMM): Theory
Covers Hidden Markov Models (HMM) for modeling time series data and decoding using the Viterbi Algorithm.
Markov Chains: Stopping Times
Explores stopping times in Markov Chains, illustrating their properties and the Strong Markov Property.
Markov Chains and Applications
Explores Markov chains and their applications in algorithms, focusing on user impatience and faithful sample generation.
Markov Chain Monte Carlo: Sampling and Convergence
Explores Markov Chain Monte Carlo for sampling high-dimensional distributions and optimizing functions using the Metropolis-Hastings algorithm.
Stochastic Models for Communications
Covers stochastic models for communications, focusing on random variables, Markov chains, Poisson processes, and probability calculations.
Markov Chains and Algorithm Applications
Explores Markov chains and algorithm applications, including exact simulation and Propp-Wilson algorithms.
Hypothesis Testing & Confidence Intervals
Covers hypothesis testing, power, confidence intervals, and small sample considerations.
Monte Carlo Markov Chains
Covers Monte Carlo Markov Chains and sampling algorithms for iterative trial configurations.
Modeling Neurobiological Signals: Spikes & Firing Rate
Explores modeling neurobiological signals, focusing on spikes, firing rate, multiple state neurons, and parameter estimation.
Markov Chains: Theory and Applications
Covers the theory and applications of Markov chains in modeling random phenomena and decision-making under uncertainty.
Monte Carlo: Optimization and Estimation
Explores optimization and estimation in Monte Carlo methods, emphasizing Bayes-optimal groups and estimators.
Markov Chains and Applications
Explores Markov chains, Ising Model, Metropolis algorithm, and Glauber dynamics.
Biased Monte Carlo Markov Chain
Explores Biased Monte Carlo Markov Chain, including Bayes-optimal estimation and Metropolis-Hastings algorithm.
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