Bayesian Statistics Courses and Certifications

Advanced Bayesian Statistics Using R

Now that you know the basics of Bayesian inference, dive deeper to explore its richness and flexibility more fully. Let’s take a closer look at modeling latent variables, Bayesian model averaging, generalised linear models, and MCMC methods

• edX
• 6 weeks, 5-10 hours a week
• On-Demand
• Free Online Course (Audit)

Introduction to Bayesian Statistics Using R

9 ratings at edX

Learn the fundamentals of Bayesian approach to data analysis, and practice answering real life questions using R.

• edX
• 6 weeks, 5-10 hours a week
• On-Demand
• Free Online Course (Audit)

Microcredential

Bayesian Statistics Using R

Practical introduction to applied Bayesian data analysis using R, covering fundamentals, real-life applications, and advanced topics like latent variables, model averaging, and MCMC methods.

• edX
• 12 weeks, 5-10 hours a week
• On-Demand
• $278.00
• 2 courses

The National Centre for Research Methods

Advanced Bayesian Methods

Explore advanced Bayesian computational techniques including Metropolis Hastings, Gibbs sampling, and convergence assessment for efficient statistical analysis and modeling.

• YouTube
• 1 hour 30 minutes
• On-Demand
• Free Video

Bayesian Theory and Graphical Models

Explore Bayesian theory and graphical models, covering key concepts, applications, and advanced topics in probabilistic reasoning and statistical inference.

• YouTube
• 2 hours 30 minutes
• On-Demand
• Free Video

Ox educ

Bayesian Statistics - A Comprehensive Course

Comprehensive introduction to Bayesian statistics, covering key concepts, probability distributions, and practical applications with minimal mathematical complexity.

• YouTube
• 6 hours
• On-Demand
• Free Video

Society for Industrial and Applied Mathematics

Bayesian Reimaging of Sparsity in Inverse Problems - SIAM-IS Virtual Seminar

Bayesian approach to sparse inverse problems in imaging and dictionary learning, featuring efficient computational framework and hierarchical prior models for sparsity promotion and automatic model reduction.

• YouTube
• 1 hour 9 minutes
• On-Demand
• Free Video

Richard McElreath

Statistical Rethinking 2023 - Missing Data

Explore missing data in DAGs and learn Bayesian imputation techniques for handling incomplete datasets in statistical analysis, with practical examples and insights.

• YouTube
• 1 hour 3 minutes
• On-Demand
• Free Video

Richard McElreath

Statistical Rethinking 2023 - Multilevel Adventures

Explore multilevel modeling techniques for analyzing clustered data, focusing on varying confounds, district-level effects, and urban-rural differences in Bangladesh fertility studies.

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

Richard McElreath

Statistical Rethinking 2023 - Categories & Curves

Explore statistical concepts like categories, posterior contrasts, and curves in this comprehensive lecture. Learn advanced techniques for data analysis and modeling using Bayesian methods.

• YouTube
• 1 hour 25 minutes
• On-Demand
• Free Video

Richard McElreath

Statistical Rethinking 2023 - The Garden of Forking Data

Explore Bayesian statistics through the "Garden of Forking Data" metaphor. Learn about generative models, probability, testing, and posterior distributions for improved statistical reasoning and prediction.

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

Richard McElreath

Statistical Rethinking 2022 - Geocentric Models

Explore Bayesian linear regression, covering model language, generative and statistical approaches, validation, and posterior predictions for a comprehensive understanding of statistical modeling.

• YouTube
• 1 hour 21 minutes
• On-Demand
• Free Video

Richard McElreath

Bayesian Statistics Without Frequentist Language

Explore Bayesian statistics from a fresh perspective, focusing on its core concepts and applications without relying on traditional frequentist terminology.

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

Institute for Pure & Applied Mathematics (IPAM)

A Brief Introduction to Nested Sampling - IPAM at UCLA

Explore Nested Sampling for Bayesian model uncertainty and selection. Learn its principles, advantages, and recent extensions like Dynamic Nested Sampling for estimating marginal likelihoods and posterior distributions.

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

MITCBMM

Bayesian Inference in Generative Models

Explore approximate Bayesian inference methods, including sampling-based techniques and variational inference, with hands-on exercises in implementing algorithms and using probabilistic programming languages.

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