PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

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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EPrint Type:Conference or Workshop Item (Poster)
Additional Information:extended abstract for an invited poster
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:2610
Deposited By:Jarkko Salojärvi
Deposited On:22 November 2006