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Sequential Monte Carlo for Graphical Models
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
University of Cambridge, Cambridge, UK.
Uppsala University, Uppsala, Sweden.
2014 (English)In: Advances in Neural Information Processing Systems, 2014, p. 1862-1870Conference paper, Published paper (Refereed)
Abstract [en]

We propose a new framework for how to use sequential Monte Carlo (SMC) algorithms for inference in probabilistic graphical models (PGM). Via a sequential decomposition of the PGM we find a sequence of auxiliary distributions defined on a monotonically increasing sequence of probability spaces. By targeting these auxiliary distributions using SMC we are able to approximate the full joint distribution defined by the PGM. One of the key merits of the SMC sampler is that it provides an unbiased estimate of the partition function of the model. We also show how it can be used within a particle Markov chain Monte Carlo framework in order to construct high-dimensional block-sampling algorithms for general PGMs.

Place, publisher, year, edition, pages
2014. p. 1862-1870
National Category
Computer Sciences Probability Theory and Statistics Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-112967OAI: oai:DiVA.org:liu-112967DiVA, id: diva2:775992
Conference
Neural Information Processing Systems (NIPS)
Available from: 2015-01-06 Created: 2015-01-06 Last updated: 2018-11-09Bibliographically approved
In thesis
1. Machine learning using approximate inference: Variational and sequential Monte Carlo methods
Open this publication in new window or tab >>Machine learning using approximate inference: Variational and sequential Monte Carlo methods
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Automatic decision making and pattern recognition under uncertainty are difficult tasks that are ubiquitous in our everyday life. The systems we design, and technology we develop, requires us to coherently represent and work with uncertainty in data. Probabilistic models and probabilistic inference gives us a powerful framework for solving this problem. Using this framework, while enticing, results in difficult-to-compute integrals and probabilities when conditioning on the observed data. This means we have a need for approximate inference, methods that solves the problem approximately using a systematic approach. In this thesis we develop new methods for efficient approximate inference in probabilistic models.

There are generally two approaches to approximate inference, variational methods and Monte Carlo methods. In Monte Carlo methods we use a large number of random samples to approximate the integral of interest. With variational methods, on the other hand, we turn the integration problem into that of an optimization problem. We develop algorithms of both types and bridge the gap between them.

First, we present a self-contained tutorial to the popular sequential Monte Carlo (SMC) class of methods. Next, we propose new algorithms and applications based on SMC for approximate inference in probabilistic graphical models. We derive nested sequential Monte Carlo, a new algorithm particularly well suited for inference in a large class of high-dimensional probabilistic models. Then, inspired by similar ideas we derive interacting particle Markov chain Monte Carlo to make use of parallelization to speed up approximate inference for universal probabilistic programming languages. After that, we show how we can make use of the rejection sampling process when generating gamma distributed random variables to speed up variational inference. Finally, we bridge the gap between SMC and variational methods by developing variational sequential Monte Carlo, a new flexible family of variational approximations.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2018. p. 39
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1969
National Category
Control Engineering Computer Sciences Signal Processing
Identifiers
urn:nbn:se:liu:diva-152647 (URN)10.3384/diss.diva-152647 (DOI)9789176851616 (ISBN)
Public defence
2018-12-14, Ada Lovelace, Building B, Campus Valla, Linköping, 10:15 (English)
Opponent
Supervisors
Available from: 2018-11-27 Created: 2018-11-09 Last updated: 2018-12-05Bibliographically approved

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Andersson Naesseth, ChristianLindsten, FredrikSchön, Thomas

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Citation style
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