This lecture covers the Beta distribution, Bayesian inference in the Bernoulli model with a Beta prior, comparison of different priors, posterior mean and variance calculation, and posterior distribution in the Beta-Bernoulli model.
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Introduces Bayesian estimation, covering classical versus Bayesian inference, conjugate priors, MCMC methods, and practical examples like temperature estimation and choice modeling.
Discusses the Dirichlet distribution, Bayesian inference, posterior mean and variance, conjugate priors, and predictive distribution in the Dirichlet-Multinomial model.
