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dc.contributor.authorKon, Mark A.en_US
dc.contributor.authorPlaskota, Leszeken_US
dc.date.accessioned2019-11-06T14:28:19Z
dc.date.available2019-11-06T14:28:19Z
dc.date.issued2012
dc.identifier.citationMark A Kon, Leszek Plaskota. 2012. "On Some Integrated Approaches to Inference." CoRR, Volume abs/1212.1180.
dc.identifier.urihttps://hdl.handle.net/2144/38439
dc.description.abstractWe present arguments for the formulation of unified approach to different standard continuous inference methods from partial information. It is claimed that an explicit partition of information into a priori (prior knowledge) and a posteriori information (data) is an important way of standardizing inference approaches so that they can be compared on a normative scale, and so that notions of optimal algorithms become farther-reaching. The inference methods considered include neural network approaches, information-based complexity, and Monte Carlo, spline, and regularization methods. The model is an extension of currently used continuous complexity models, with a class of algorithms in the form of optimization methods, in which an optimization functional (involving the data) is minimized. This extends the family of current approaches in continuous complexity theory, which include the use of interpolatory algorithms in worst and average case settings.en_US
dc.relation.ispartofCoRR
dc.subjectStatisticsen_US
dc.subjectMachine learningen_US
dc.titleOn some integrated approaches to inferenceen_US
dc.typeArticleen_US
dc.description.versionFirst author draften_US
pubs.elements-sourcedblpen_US
pubs.notesEmbargo: Not knownen_US
pubs.organisational-groupBoston Universityen_US
pubs.organisational-groupBoston University, College of Arts & Sciencesen_US
pubs.organisational-groupBoston University, College of Arts & Sciences, Department of Mathematics & Statisticsen_US
dc.identifier.orcid0000-0001-5902-9412 (Kon, Mark A)
dc.identifier.mycv61326


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