MCMC with Metropolis

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Covers decoding methods and training challenges in natural language generation.

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

Explores max entropy, Shannon's entropy, Lagrange multipliers, and Monte Carlo sampling techniques.

Explores sampling a probability distribution and structure functions in statistical analysis.

Covers Markov chains and their applications in algorithms, focusing on Markov Chain Monte Carlo sampling and the Metropolis-Hastings algorithm.

Covers the basics of Markov chains and their algorithmic applications.

Explores challenges in natural language generation, decoding algorithms, training issues, and reward functions.

Covers the characteristics and importance of the normal distribution, including examples and treatment scenarios.

Explores optimization and estimation in Monte Carlo methods, emphasizing Bayes-optimal groups and estimators.

Covers the solutions to the midterm exam of a Probability Theory course, including calculations of probabilities and expectations.