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Predictive hypothesis identification

Hutter, Marcus

Description

While statistics focusses on hypothesis testing and on estimating (properties of) the true sampling distribution, in machine learning the performance of learning algorithms on future data is the primary issue. In this paper we bridge the gap with a general principle (PHI) that identifies hypotheses with best predictive performance. This includes predictive point and interval estimation, simple and composite hypothesis testing, (mixture) model selection, and others as special cases. For...[Show more]

dc.contributor.authorHutter, Marcus
dc.date.accessioned2015-08-26T06:28:29Z
dc.date.available2015-08-26T06:28:29Z
dc.identifier.urihttp://hdl.handle.net/1885/14970
dc.description.abstractWhile statistics focusses on hypothesis testing and on estimating (properties of) the true sampling distribution, in machine learning the performance of learning algorithms on future data is the primary issue. In this paper we bridge the gap with a general principle (PHI) that identifies hypotheses with best predictive performance. This includes predictive point and interval estimation, simple and composite hypothesis testing, (mixture) model selection, and others as special cases. For concrete instantiations we will recover well-known methods, variations thereof, and new ones. PHI nicely justifies, reconciles, and blends (a reparametrization invariant variation of) MAP, ML, MDL, and moment estimation. One particular feature of PHI is that it can genuinely deal with nested hypotheses.
dc.relation.ispartofNinth Valencia International Meeting on Bayesian Statistics and 2010 ISBA World Meeting
dc.rights© The Author(s)
dc.subjectparameter estimation
dc.subjecthypothesis testing
dc.subjectmodel selection
dc.subjectpredictive inference
dc.subjectcomposite hypotheses
dc.subjectMAP versus ML
dc.subjectmoment fitting
dc.subjectBayesian statistics
dc.titlePredictive hypothesis identification
dc.typeConference paper
dc.date.issued2008-09
local.type.statusSubmitted Version
local.contributor.affiliationHutter, M., Research School of Computer Science, The Australian National University
local.bibliographicCitation.startpage1
local.bibliographicCitation.lastpage16
CollectionsANU Research Publications

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