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Variational Sequent...
Variational Sequential Monte Carlo
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- Andersson Naesseth, Christian, 1986- (författare)
- Linköpings universitet,Reglerteknik,Tekniska fakulteten
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- Linderman, Scott (författare)
- Columbia University, New York City, New York, United States
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- Ranganath, Rajesh (författare)
- New York University, New York City, New York, United States
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- Blei, David (författare)
- Columbia University, New York City, New York, United States
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(creator_code:org_t)
- PMLR, 2018
- 2018
- Engelska.
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Ingår i: Proceedings of International Conference on Artificial Intelligence and Statistics, 9-11 April 2018, Playa Blanca, Lanzarote, Canary Islands. - : PMLR. ; , s. 968-977
- Relaterad länk:
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https://liu.diva-por... (primary) (Raw object)
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https://urn.kb.se/re...
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Abstract
Ämnesord
Stäng
- Many recent advances in large scale probabilistic inference rely on variational methods. The success of variational approaches depends on (i) formulating a flexible parametric family of distributions, and (ii) optimizing the parameters to find the member of this family that most closely approximates the exact posterior. In this paper we present a new approximating family of distributions, the variational sequential Monte Carlo (VSMC) family, and show how to optimize it in variational inference. VSMC melds variational inference (VI) and sequential Monte Carlo (SMC), providing practitioners with flexible, accurate, and powerful Bayesian inference. The VSMC family is a variational family that can approximate the posterior arbitrarily well, while still allowing for efficient optimization of its parameters. We demonstrate its utility on state space models, stochastic volatility models for financial data, and deep Markov models of brain neural circuits.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)