Sequential Monte Carlo methods for graphical models

Duration: 34 mins 9 secs

Share this media item:

Embed this media item:

<iframe width="_width_" height="_height_" src="https://sms.cam.ac.uk/media/1707758/embed" frameborder="0" scrolling="no" allowfullscreen></iframe>

Choose size:

About this item

Available Formats

About this item

Description:	Schön, TB (Uppsala Universitet) Friday 25 April 2014, 10:40-11:15


Created:	2014-04-29 15:41
Collection:	Advanced Monte Carlo Methods for Complex Inference Problems
Publisher:	Isaac Newton Institute
Copyright:	Schön, TB
Language:	eng (English)


Abstract:	Co-authors: Christian A. Naesseth (Linkoping University), Fredrik Lindsten (University of Cambridge) We develop a sequential Monte Carlo (SMC) algorithm for inference in general probabilistic graphical model. 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 purpose built SMC samplers we are able to approximate the full joint distribution defined by the graphical model. Our SMC sampler also provides an unbiased estimate of the partition function (normalization constant) and we show how it can be used within a particle Markov chain Monte Carlo framework. This allows for better approximations of the marginals and for unknown parameters to be estimated. The proposed inference algorithms can deal with an arbitrary graph structure and the domain of the random variables in the graph can be discrete or continuous. Related Links: http://arxiv.org/pdf/1402.0330v1.pdf - Associated paper http://user.it.uu.se/~thosc112/index.html - Speaker (Thomas Schön) home page

Abstract:

Co-authors: Christian A. Naesseth (Linkoping University), Fredrik Lindsten (University of Cambridge)
We develop a sequential Monte Carlo (SMC) algorithm for inference in general probabilistic graphical model. 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 purpose built SMC samplers we are able to approximate the full joint distribution defined by the graphical model. Our SMC sampler also provides an unbiased estimate of the partition function (normalization constant) and we show how it can be used within a particle Markov chain Monte Carlo framework. This allows for better approximations of the marginals and for unknown parameters to be estimated. The proposed inference algorithms can deal with an arbitrary graph structure and the domain of the random variables in the graph can be discrete or continuous.

Related Links: http://arxiv.org/pdf/1402.0330v1.pdf - Associated paper http://user.it.uu.se/~thosc112/index.html - Speaker (Thomas Schön) home page

Available Formats

Format	Quality	Bitrate	Size
MPEG-4 Video	640x360	1.93 Mbits/sec	496.07 MB	View	Download
WebM	640x360	517.28 kbits/sec	129.45 MB	View	Download
iPod Video	480x270	522.22 kbits/sec	130.62 MB	View	Download
MP3	44100 Hz	249.77 kbits/sec	62.54 MB	Listen	Download
Auto *	(Allows browser to choose a format it supports)