This lecture covers the concept of exponential families, maximum entropy distribution, and the Moxwell-Boltzmann distribution. It explains the properties of sufficient statistics, normalization constants, and conjugate priors.
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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.
