Connect Bayesian inference and learning to posterior computation with Markov chain Monte Carlo.
Progress from Bayesian inference and learning to posterior computation with Markov chain Monte Carlo. Learners articulate prior, likelihood, and posterior assumptions, distinguish inference targets from computational approximations, and assess how sampling diagnostics and model specification shape conclusions. Emphasis is placed on uncertainty and sensitivity analysis rather than treating a numerical posterior as automatically trustworthy. Original notes are not transcripts.
Construct and interpret a posterior from a prior and likelihood while stating model assumptions. Distinguish posterior learning objectives from algorithms used to approximate posterior quantities. Assess MCMC convergence and Monte Carlo uncertainty with diagnostics and sensitivity checks.
Prior familiarity with probability, statistics, and supervised learning is recommended. Comfort with conditional probability, likelihoods, and mathematical notation helps; prior MCMC implementation is not required.
Experienced learners who want to deepen and apply advanced AI skills
People who learn best through examples, guided lessons, and hands-on practice
Professionals, creators, and independent builders looking for a repeatable workflow
Lesson 1 is free
4 lessons · Advanced · Full course.
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