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Black Box Variational Inference : = Scalable, Generic Bayesian Computation and its Applications.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Black Box Variational Inference :/
其他題名:	Scalable, Generic Bayesian Computation and its Applications.
作者:	Ranganath, Rajesh.
面頁冊數:	1 online resource (164 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-05(E), Section: B.
Contained By:	Dissertation Abstracts International79-05B(E).
標題:	Computer science. -
電子資源:	click for full text (PQDT)
ISBN:	9780355480566

Black Box Variational Inference : = Scalable, Generic Bayesian Computation and its Applications.
Ranganath, Rajesh.

Black Box Variational Inference :Scalable, Generic Bayesian Computation and its Applications. - 1 online resource (164 pages)

Source: Dissertation Abstracts International, Volume: 79-05(E), Section: B.

Thesis (Ph.D.)

Includes bibliographical references

This item is not available from ProQuest Dissertations & Theses.

Probabilistic generative models are robust to noise, uncover unseen patterns, and make predictions about the future. These models have been used successfully to solve problems in neuroscience, astrophysics, genetics, and medicine. The main computational challenge is computing the hidden structure given the data---posterior inference. For most models of interest, computing the posterior distribution requires approximations like variational inference. Variational inference transforms posterior inference into optimization. Classically, this optimization problem was feasible to deploy in only a small fraction of models.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355480566Subjects--Topical Terms:

573171
Computer science.
Index Terms--Genre/Form:

554714
Electronic books.

Black Box Variational Inference : = Scalable, Generic Bayesian Computation and its Applications.
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500 $a Source: Dissertation Abstracts International, Volume: 79-05(E), Section: B.
500 $a Adviser: David M. Blei.
502 $a Thesis (Ph.D.) $c Princeton University $d 2017.
504 $a Includes bibliographical references
506 $a This item is not available from ProQuest Dissertations & Theses.
520 $a Probabilistic generative models are robust to noise, uncover unseen patterns, and make predictions about the future. These models have been used successfully to solve problems in neuroscience, astrophysics, genetics, and medicine. The main computational challenge is computing the hidden structure given the data---posterior inference. For most models of interest, computing the posterior distribution requires approximations like variational inference. Variational inference transforms posterior inference into optimization. Classically, this optimization problem was feasible to deploy in only a small fraction of models.
520 $a This thesis develops black box variational inference. Black box variational inference is a variational inference algorithm that is easy to deploy on a broad class of models and has already found use in models for neuroscience and health care. It makes new kinds of models possible, ones that were too unruly for previous inference methods.
520 $a One set of models we develop is deep exponential families. Deep exponential families uncover new kinds of hidden patterns while being predictive of future data. Many existing models are deep exponential families. Black box variational inference makes it possible for us to quickly study a broad range of deep exponential families with minimal added effort for each new type of deep exponential family.
520 $a The ideas around black box variational inference also facilitate new kinds of variational methods. First, we develop hierarchical variational models. Hierarchical variational models improve the approximation quality of variational inference by building higher-fidelity approximations from coarser ones. We show that they help with inference in deep exponential families. Second, we introduce operator variational inference. Operator variational inference delves into the possible distance measures that can be used for the variational optimization problem. We show that this formulation categorizes various variational inference methods and enables variational approximations without tractable densities.
520 $a By developing black box variational inference, we have opened doors to new models, better posterior approximations, and new varieties of variational inference algorithms.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
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773 0 $t Dissertation Abstracts International $g 79-05B(E).
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