Discriminative MCMC
Kai Puolamäki, Jarkko Salojärvi, Eerika Savia and Samuel Kaski
In: The Learning Workshop, 04 Apr - 07 Apr 2006, Snowbird, Utah, USA.

Abstract

We discuss Bayesian modeling in the case where the model is incorrect. Standard posterior distribution is optimal for inference if the true model is within the model family. In the case of an incorrect model, we show that for inference on conditioned distribution, a different posterior-type distribution is optimal. We provide here an axiomatic justification of previously suggested supervised posterior distribution, introduce Markov Chain Monte Carlo -type methods for computing with the posterior, and demonstrate empirically that it works as expected.

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