دانلود رایگان پاورپوینت یادگیری در شبکه بیزی (انگلیسی)

Learning In Bayesian Networks
General Learning Problem
Classes Of Graphical Model Learning Problems
If Network Structure Is Known, The Problem Involves Learning Distributions
Learning CPDs When All Variables Are Observed And Network Structure Is Known
Recasting Learning As Bayesian Inference
Recasting Learning As Inference
Treating Conditional Probabilities As Latent Variables
General Approach: Bayesian Learning of Probabilities in a Bayes Net
General Approach: Bayesian Learning of Probabilities in a Bayes Net
Computing Parameter Posteriors
Terminology Digression
Generalizing To Categorical RVs In Bayes Net
Prediction Directly From Data:Categorical Random Variables
Other Easy Cases

Recasting Learning As Bayesian Inference

We’ve already used Bayesian inference in probabilistic models to compute posteriors on latent (a.k.a. hidden, nonobservable) variables from data. E.g., Weiss model Direction of motion E.g., Gaussian mixture model To which cluster does each data point belong Why not treat unknown parameters in the same way? E.g., Gaussian mixture parameters E.g., entries in conditional prob. tables