Gibbs Sampling Courses and Certifications

Institute for Pure & Applied Mathematics (IPAM)

Gibbs Sampling for Convex Bodies and an L_0 Isoperimetric Inequality

Explore Gibbs sampling for high-dimensional distributions, focusing on convergence guarantees and conductance bounds for sampling from convex bodies. Gain insights into this widely-used algorithm's efficiency and limitations.

• YouTube
• 26 minutes
• On-Demand
• Free Video

Simons Institute

Scalable and Reliable Inference for Probabilistic Modeling

Explore scalable and reliable inference techniques for probabilistic modeling, focusing on Metropolis-Hastings, Gibbs sampling, and stochastic gradient MCMC methods for efficient data analysis and deep learning applications.

• YouTube
• 42 minutes
• On-Demand
• Free Video

Serrano.Academy

Training Latent Dirichlet Allocation: Gibbs Sampling - Part 2

Explore Gibbs sampling for training Latent Dirichlet Allocation models, learning to sort documents into topics efficiently through practical exercises and in-depth explanations.

• YouTube
• 27 minutes
• On-Demand
• Free Video

Aladdin Persson

Latent Dirichlet Allocation with Gibbs Sampling Explained

Comprehensive explanation of Latent Dirichlet Allocation (LDA) with Gibbs Sampling, covering topic modeling, posterior inference, and implementation details. Ideal for those interested in understanding this classic machine learning technique.

• YouTube
• 33 minutes
• On-Demand
• Free Video

Probabilistic Graphical Models 2: Inference

488 ratings at Coursera

Explore probabilistic inference in graphical models, covering exact and approximate algorithms for conditional probability queries and MAP inference, with hands-on programming assignments.

• Coursera
• 38 hours
• On-Demand
• Free Online Course (Audit)

Simons Institute

Quantum Computational Advantage With Constant-Temperature Gibbs Sampling

Explore quantum computational advantage through constant-temperature Gibbs sampling, examining thermalization processes and their implications for quantum systems and classical algorithms.

• YouTube
• 38 minutes
• On-Demand
• Free Video

Simons Institute

Gibbs State Preparation for Commuting Hamiltonian - Mapping to Classical Gibbs Sampling

Explore quantum Gibbs state preparation through mapping commuting Hamiltonians to classical systems, focusing on efficient sampling methods and applications in quantum computing.

• YouTube
• 51 minutes
• On-Demand
• Free Video

Simons Institute

Exactly Detailed-Balanced Quasi-Local Quantum Dynamics for Gibbs Sampling

Explore quantum dynamics and Gibbs sampling through detailed-balanced systems, focusing on mathematical approaches to quantum algorithms and open quantum systems.

• YouTube
• 1 hour
• On-Demand
• Free Video

INI Seminar Room 2

High-Probability Mixing Time Bound for Gibbs Sampling from Log-Smooth Strongly Log-Concave Distributions

Delve into advanced mathematical analysis of Gibbs sampling for log-smooth strongly log-concave distributions, exploring mixing time bounds and high-probability isoperimetric inequalities in n-dimensional space.

• YouTube
• 1 hour 8 minutes
• On-Demand
• Free Video

Bayesian Statistics

610 ratings at Udemy

Bayes Theorem, Bayesian networks, Bayesian sampling methods, Bayesian inference, machine learning and much more

• Udemy
• 4 hours 34 minutes
• On-Demand
• Paid Course

Bayesian Networks 2 - Forward-Backward - Stanford CS221: AI

Explore advanced probabilistic inference techniques, including hidden Markov models, particle filtering, and Gibbs sampling, for AI applications in object tracking and more.

• YouTube
• 1 hour 12 minutes
• On-Demand
• Free Online Course

Simons Institute

Quantum Gibbs Sampling

Explore quantum algorithms for sampling quantum Gibbs states, a fundamental concept in quantum computing and statistical mechanics, with applications in computer science.

• YouTube
• 1 hour 26 minutes
• On-Demand
• Free Video

Serrano.Academy

Restricted Boltzmann Machines - A Friendly Introduction

Friendly introduction to Restricted Boltzmann Machines using real-life examples. Covers key concepts like probabilities, training, contrastive divergence, and Gibbs sampling for machine learning enthusiasts.

• YouTube
• 37 minutes
• On-Demand
• Free Video

Institute for Pure & Applied Mathematics (IPAM)

Restricted Boltzmann Machines for Maximum Satisfiability - IPAM at UCLA

Explore how Restricted Boltzmann Machines can be applied to Maximum Satisfiability problems, leveraging neural network accelerators for efficient stochastic search in combinatorial optimization.

• YouTube
• 56 minutes
• On-Demand
• Free Video

Joint Mathematics Meetings

Alternate Minimization and Scaling Algorithms - Theory, Applications and Connections Across Mathematics and Computer Science

Explore alternate minimization and scaling algorithms, their applications, and connections across mathematics and computer science with Avi Wigderson's in-depth lecture.

• YouTube
• 52 minutes
• On-Demand
• Conference Talk