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Measure Transformer...
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Borgström, JohannesMicrosoft Research, Cambridge
(författare)
Measure Transformer Semantics for Bayesian Machine Learning
- Artikel/kapitelEngelska2011
Förlag, utgivningsår, omfång ...
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Berlin, Heidelberg :Springer-Verlag,2011
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electronicrdacarrier
Nummerbeteckningar
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LIBRIS-ID:oai:DiVA.org:uu-161498
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https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-161498URI
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https://doi.org/10.1007/978-3-642-19718-5_5DOI
Kompletterande språkuppgifter
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Språk:engelska
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Sammanfattning på:engelska
Ingår i deldatabas
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Ämneskategori:ref swepub-contenttype
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Ämneskategori:kon swepub-publicationtype
Anmärkningar
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The Bayesian approach to machine learning amounts to inferring posterior distributions of random variables from a probabilistic model of how the variables are related (that is, a prior distribution) and a set of observations of variables. There is a trend in machine learning towards expressing Bayesian models as probabilistic programs. As a foundation for this kind of programming, we propose a core functional calculus with primitives for sampling prior distributions and observing variables. We define combinators for measure transformers, based on theorems in measure theory, and use these to give a rigorous semantics to our core calculus. The original features of our semantics include its support for discrete, continuous, and hybrid measures, and, in particular, for observations of zero-probability events. We compile our core language to a small imperative language that has a straightforward semantics via factor graphs, data structures that enable many efficient inference algorithms. We use an existing inference engine for efficient approximate inference of posterior marginal distributions, treating thousands of observations per second for large instances of realistic models.
Ämnesord och genrebeteckningar
Biuppslag (personer, institutioner, konferenser, titlar ...)
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Gordon, Andrew DMicrosoft Research, Cambridge
(författare)
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Greenberg, MichaelUniversity of Pennsylvania
(författare)
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Margetson, JamesMicrosoft Research, Cambridge
(författare)
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van Gael, JurgenMicrosoft Research, Cambridge
(författare)
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Microsoft Research, CambridgeUniversity of Pennsylvania
(creator_code:org_t)
Sammanhörande titlar
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Ingår i:20th European Symposium on ProgrammingBerlin, Heidelberg : Springer-Verlag, s. 77-96
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