BayesianModelComparison

BayesianModelComparison - Machine Learning Srihari Bayesian...

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Machine Learning Srihari Bayesian Model Comparison Sargur Srihari srihari@cedar.buffalo.edu 1
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Machine Learning Srihari Motivation • In frequentist setting – Over-fitting is a problem – Cross-validation used for • Setting values for regularization parameters • Choosing between alternative models • Bayesian model selection – Used to determine regularization parameters 2
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Machine Learning Srihari Bayesian Perspective • Avoids over-fitting – By marginalizing over model parameters • sum over model parameters instead of point estimates • Models compared directly over training data – No need for validation set – Allows use of all available data in training set – Avoids multiple training runs for each model associated with cross-validation – Allows multiple complexity parameters to be simultaneously determined during training • Relevance vector m/c is Bayesian model with one complexity parameter for every data point 3
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Machine Learning Srihari Model Selection based on Model Evidence • Probabilities used to represent uncertainty in choice of model • Compare a set of models M i , i=1,. .L • A model is a probability distribution over observed data D – E.g., in polynomial curve-fitting distribution over target
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BayesianModelComparison - Machine Learning Srihari Bayesian...

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