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001440466 001__ 1440466
001440466 003__ OCoLC
001440466 005__ 20230309004605.0
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001440466 019__ $$a1277278646$$a1280046152$$a1280105334$$a1287763880
001440466 020__ $$a9783030828080$$q(electronic bk.)
001440466 020__ $$a3030828085$$q(electronic bk.)
001440466 020__ $$z9783030828073$$q(print)
001440466 020__ $$z3030828077
001440466 0247_ $$a10.1007/978-3-030-82808-0$$2doi
001440466 035__ $$aSP(OCoLC)1280127782
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001440466 049__ $$aISEA
001440466 050_4 $$aQA279.5
001440466 08204 $$a519.5/42$$223
001440466 1001_ $$aHeard, Nicholas,$$eauthor.
001440466 24513 $$aAn introduction to Bayesian inference, methods and computation /$$cNick Heard.
001440466 264_1 $$aCham, Switzerland :$$bSpringer,$$c2021.
001440466 300__ $$a1 online resource (xii, 169 pages) :$$billustrations (some color)
001440466 336__ $$atext$$btxt$$2rdacontent
001440466 337__ $$acomputer$$bc$$2rdamedia
001440466 338__ $$aonline resource$$bcr$$2rdacarrier
001440466 347__ $$atext file
001440466 347__ $$bPDF
001440466 504__ $$aIncludes bibliographical references and index.
001440466 5050_ $$aUncertainty and Decisions -- Prior and Likelihood Representation -- Graphical Modeling -- Parametric Models -- Computational Inference -- Bayesian Software Packages -- Model choice -- Linear Models -- Nonparametric Models -- Nonparametric Regression -- Clustering and Latent Factor Models -- Conjugate Parametric Models.
001440466 506__ $$aAccess limited to authorized users.
001440466 520__ $$aThese lecture notes provide a rapid, accessible introduction to Bayesian statistical methods. The course covers the fundamental philosophy and principles of Bayesian inference, including the reasoning behind the prior/likelihood model construction synonymous with Bayesian methods, through to advanced topics such as nonparametrics, Gaussian processes and latent factor models. These advanced modelling techniques can easily be applied using computer code samples written in Python and Stan which are integrated into the main text. Importantly, the reader will learn methods for assessing model fit, and to choose between rival modelling approaches.
001440466 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed October 22, 2021).
001440466 650_0 $$aBayesian statistical decision theory.
001440466 650_6 $$aThéorie de la décision bayésienne.
001440466 655_0 $$aElectronic books.
001440466 77608 $$iPrint version:$$aHeard, Nicholas.$$tIntroduction to Bayesian inference, methods and computation.$$dCham, Switzerland : Springer, 2021$$z3030828077$$z9783030828073$$w(OCoLC)1259046207
001440466 852__ $$bebk
001440466 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-82808-0$$zOnline Access$$91397441.1
001440466 909CO $$ooai:library.usi.edu:1440466$$pGLOBAL_SET
001440466 980__ $$aBIB
001440466 980__ $$aEBOOK
001440466 982__ $$aEbook
001440466 983__ $$aOnline
001440466 994__ $$a92$$bISE