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Fast Non-Parametric Bayesian Inference on Infinite Trees

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dc.contributor.authorHutter, Marcus
dc.coverage.spatialBarbados
dc.date.accessioned2015-12-10T22:41:30Z
dc.date.created6-8 January 2005
dc.date.issued2005
dc.date.updated2016-02-24T11:44:55Z
dc.description.abstractGiven i.i.d. data from an unknown distribution, we consider the problem of predicting future items. An adaptive way to estimate the probability density is to recursively subdivide the domain to an appropriate data-dependent granularity. A Bayesian would assign a data-independent prior probability to "subdivide", which leads to a prior over infinite(ly many) trees. We derive an exact, fast, and simple inference algorithm for such a prior, for the data evidence, the predictive distribution, the effective model dimension, and other quantities.
dc.identifier.isbn097273581X
dc.identifier.urihttp://hdl.handle.net/1885/57947
dc.publisherSociety for Artificial Intelligence and Statistics
dc.relation.ispartofseriesInternational Conference on Artificial Intelligence and Statistics (AISTATS 2005)
dc.rightsCopyright Information: © The Author(s)
dc.sourceProceedings of the 10th International Workshop on Artificial Intelligence and Statistics (AISTATS 2005)
dc.source.urihttp://www.gatsby.ucl.ac.uk/aistats
dc.subjectKeywords: Inference algorithm; Infinite trees; Non-parametric Bayesian; Predictive distributions; Prior probability; Probability densities; Artificial intelligence; Bayesian networks; Forestry; Inference engines; Probability density function; Trees (mathematics)
dc.titleFast Non-Parametric Bayesian Inference on Infinite Trees
dc.typeConference paper
local.bibliographicCitation.lastpage151
local.bibliographicCitation.startpage144
local.contributor.affiliationHutter, Marcus, College of Engineering and Computer Science, ANU
local.contributor.authoruidHutter, Marcus, u4350841
local.description.embargo2037-12-31
local.description.notesImported from ARIES
local.description.refereedYes
local.identifier.absfor080109 - Pattern Recognition and Data Mining
local.identifier.ariespublicationu8803936xPUB421
local.identifier.scopusID2-s2.0-84862594932
local.type.statusPublished Version

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