LM101-077: How to Choose the Best Model using BIC

In this 77th episode of , we explain the proper semantic interpretation of the Bayesian Information Criterion (BIC) and emphasize how this semantic interpretation is fundamentally different from AIC (Akaike Information Criterion) model selection methods. Briefly, BIC is used to estimate the probability of the training data given the probability model, while AIC is used to estimate out-of-sample prediction error. The probability of the training data given the model is called the "marginal likelihood". Using the marginal likelihood, one can calculate the probability of a model given the training data and then use this analysis to support selecting the most probable model, selecting a model that minimizes expected risk, and support Bayesian model averaging. The assumptions which are required for BIC to be a valid approximation for the probability of the training data given the probability model are also discussed.

You can listen to LM101-077: How to Choose the Best Model using BIC online on Radio and Podcast. Open the player on this page to stream the available audio.

LM101-077: How to Choose the Best Model using BIC is an episode from Learning Machines 101 by Richard M. Golden.

This episode is 24:15 long.

This episode was published on May 2, 2019.

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