Lectures related to Markov Chain Monte Carlo

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.

Probability Theory: Midterm Solutions

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

Markov Chains: Applications and Sampling Methods

Covers the basics of Markov chains and their algorithmic applications.

Monte-Carlo Integration: Approximation and Variance MOOC: Selected Topics on Discrete Choice

Explores Monte-Carlo integration for approximating expectations and variances using random sampling and discusses error components in conditional choice models.

Max Entropy and Monte Carlo

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

Monte Carlo: Optimization and Estimation

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

Natural Language Generation: Decoding & Training

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

Sampling strategies MOOC: Selected Topics on Discrete Choice

Explores research process, variable types, causality vs correlation, and sampling strategies.

Concentration Inequalities

Covers concentration inequalities and sampling methods for estimating unknown distributions, with a focus on population infection rates.

Monte Carlo Markov Chains

Covers the theory of Markov chains and Monte Carlo methods.

Quasi-Stationary Distribution: Molecular Dynamics Modeling

Explores the quasi-stationary distribution approach in molecular dynamics modeling, covering Langevin dynamics, metastability, and kinetic Monte Carlo models.

Estimation Methods: BLP and Control-Function

Covers the inconsistency in estimates due to endogeneity and introduces the BLP and Control-Function estimation methods.

Density of States and Bayesian Inference in Computational Mathematics

Explores computing density of states and Bayesian inference using importance sampling, showcasing lower variance and parallelizability of the proposed method.

Ceramics: Powder Characterization

Explores the characterization of powders in ceramics, emphasizing the impact on ceramic properties and the manufacturing process.

Determinantal Point Processes and Extrapolation

Covers determinantal point processes, sine-process, and their extrapolation in different spaces.

Generative Models: Self-Attention and Transformers

Covers generative models with a focus on self-attention and transformers, discussing sampling methods and empirical means.

Generative Models: Boltzmann Machine

Covers generative models, focusing on Boltzmann machines and constrained maximization using Lagrange multipliers.

Probability & Stochastic Processes Log in to Mediaspace to watch this video

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

Normal Distribution: Characteristics and Examples Log in to Mediaspace to watch this video

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

Normal Distribution: Characteristics and Z-scores Log in to Mediaspace to watch this video

Explores normal distribution characteristics, Z-scores, probability in inferential statistics, sample effects, and binomial distribution approximation.