Fast exact Bayesian inference for high-dimensional models

Ferreira, JF ORCID: https://orcid.org/0000-0002-2510-2412, Lanillos, P and Dias, J, 2015. Fast exact Bayesian inference for high-dimensional models. In: 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2015): Workshop on Unconventional Computing for Bayesian Inference, Hamburg, Germany, 28 September 2015.

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Abstract

In this text, we present the principles that allow the tractable implementation of exact inference processes concerning a group of widespread classes of Bayesian generative models, which have until recently been deemed as intractable whenever formulated using high-dimensional joint distributions. We will demonstrate the usefulness of such a principled approach with an example of real-time OpenCL implementation using GPUs of a full-fledged, computer vision-based model to estimate gaze direction in human-robot interaction (HRI).

Item Type:	Conference contribution
Creators:	Ferreira, J.F., Lanillos, P. and Dias, J.
Date:	2015
Divisions:	Schools > School of Science and Technology
Record created by:	Jonathan Gallacher
Date Added:	05 Apr 2018 11:10
Last Modified:	05 Apr 2018 11:17
URI:	https://irep.ntu.ac.uk/id/eprint/33189

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