Conjugate prior - PowerPoint PPT Presentation

Bayesian Essentials and Bayesian Regression

Bayesian Essentials and Bayesian Regression. Y. 1. 1. X. Distribution Theory 101. Marginal and Conditional Distributions:. uniform. Simulating from Joint. To draw from the joint: i. draw from marginal on X ii. Condition on this draw, and draw from conditional of Y|X.

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Applied Bayesian Analysis for the Social Sciences

Applied Bayesian Analysis for the Social Sciences. Philip Pendergast Computing and Research Services Department of Sociology philip.pendergast@colorado.edu. Sponsored by Computing and Research Services and the Institute of Behavioral Science.

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Tooth Ache BN

Ache. Probe. Cavity. Tooth Ache BN. Cavity: yes, no Tooth Aches: yes, no Dental Probe: catches, doesn’t Conditional independences. Is this a naïve Bayes structure? How many distributions? How many parameters? What kind of distributions? Do we have prior beliefs about the distributions?

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248 views • 8 slides

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