Show simple item record

dc.contributor.advisorMacKay, David J. C.
dc.contributor.authorHennig, Philipp
dc.date.accessioned2011-05-25T13:15:07Z
dc.date.available2011-05-25T13:15:07Z
dc.date.issued2011-02-08
dc.identifier.otherPhD.33997
dc.identifier.urihttp://www.dspace.cam.ac.uk/handle/1810/237251
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/237251
dc.description.abstractProbability theory provides a mathematically rigorous yet conceptually flexible calculus of uncertainty, allowing the construction of complex hierarchical models for real-world inference tasks. Unfortunately, exact inference in probabilistic models is often computationally expensive or even intractable. A close inspection in such situations often reveals that computational bottlenecks are confined to certain aspects of the model, which can be circumvented by approximations without having to sacrifice the model's interesting aspects. The conceptual framework of graphical models provides an elegant means of representing probabilistic models and deriving both exact and approximate inference algorithms in terms of local computations. This makes graphical models an ideal aid in the development of generalizable approximations. This thesis contains a brief introduction to approximate inference in graphical models (Chapter 2), followed by three extensive case studies in which approximate inference algorithms are developed for challenging applied inference problems. Chapter 3 derives the first probabilistic game tree search algorithm. Chapter 4 provides a novel expressive model for inference in psychometric questionnaires. Chapter 5 develops a model for the topics of large corpora of text documents, conditional on document metadata, with a focus on computational speed. In each case, graphical models help in two important ways: They first provide important structural insight into the problem; and then suggest practical approximations to the exact probabilistic solution.en_GB
dc.description.sponsorshipThis work was supported by a scholarship from Microsoft Research, Ltd.en_GB
dc.language.isoenen_GB
dc.rightsAll Rights Reserveden
dc.rights.urihttps://www.rioxx.net/licenses/all-rights-reserved/en
dc.subjectApplied mathematicsen_GB
dc.subjectComputer scienceen_GB
dc.subjectProbability theoryen_GB
dc.subjectProbabilistic inferenceen_GB
dc.subjectGraphical modelsen_GB
dc.subjectApproximate inferenceen_GB
dc.titleApproximate inference in graphical modelsen_GB
dc.typeThesisen_GB
dc.type.qualificationlevelDoctoral
dc.type.qualificationnameDoctor of Philosophy (PhD)
dc.publisher.institutionUniversity of Cambridgeen_GB
dc.publisher.departmentDepartment of Physicsen_GB
dc.publisher.departmentCavendish Laboratoryen_GB
dc.publisher.departmentRobinson Collegeen_GB
dc.rights.generalCopyright Philipp Hennig, 2010.en_GB
dc.identifier.doi10.17863/CAM.16567


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record