Lectures related to MCMC with Metropolis

Natural Language Generation: Decoding Techniques and Training Challenges

Covers decoding methods and training challenges in natural language generation.

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

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

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: Applications and Sampling Methods

Covers the basics of Markov chains and their algorithmic applications.

Natural Language Generation: Decoding & Training

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

Monte Carlo: Optimization and Estimation

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

Probability Theory: Midterm Solutions

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

Natural Language Generation: Task

On NLG covers basics, autoregressive models, decoding methods, and challenges in text generation.

Boltzmann Machine

Introduces the Boltzmann Machine, covering expectation consistency, data clustering, and probability distribution functions.

Statistical Estimation Methods

Covers statistical estimation methods, including maximum likelihood and Bayesian estimation.

Multivariate Statistics: Normal Distribution

Introduces multivariate statistics, covering normal distribution properties and characteristic functions.

Bayesian Estimation: Overview and Examples

Introduces Bayesian estimation, covering classical versus Bayesian inference, conjugate priors, MCMC methods, and practical examples like temperature estimation and choice modeling.

Sampling Theory: Statistics for Mathematicians

Covers the theory of sampling, focusing on statistics for mathematicians.

Quantiles, Sampling, Histogram Density

Explores quantiles, sampling, and histogram density for understanding distributions and constructing confidence intervals.

Markov Chain: Configuration Sampling

Introduces the concept of a Markov process and chain in configuration sampling.

Canonical Ensemble: Probability Distribution

Explores the probability distribution in the canonical ensemble and the Boltzmann distribution.

Theory of MCMC

Covers the theory of Markov Chain Monte Carlo (MCMC) sampling and discusses convergence conditions, transition matrix choice, and target distribution evolution.

Generative Models: Crash Course

Offers a crash course on generative models, covering exponential families, sampling methods, and Metropolis algorithm.

Common Distributions: Moment Generating Functions

Explores common probability distributions, special distributions, and entropy concepts.