Brief survey on computational solutions for Bayesian inference

Alves, J.D., Ferreira, J.F. ORCID: 0000-0002-2510-2412, Lobo, J. and Dias, J., 2015. Brief survey on computational solutions for Bayesian inference. 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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In this paper, we present a brief review of research work attempting to tackle the issue of tractability in Bayesian inference, including an analysis of the applicability and trade-offs of each proposed solution. In recent years, the Bayesian approach has become increasingly popular, endowing autonomous systems with the ability to deal with uncertainty and incompleteness. However, these systems are also expected to be efficient, while Bayesian inference in general is known to be an NP-hard problem, making it paramount to develop approaches dealing with this complexity in order to allow the implementation of usable Bayesian solutions. Novel computational paradigms and also major developments in massively parallel computation technologies, such as multi-core processors, GPUs and FPGAs, provide us with an inkling of the roadmap in Bayesian computation for upcoming years.

Item Type: Conference contribution
Creators: Alves, J.D., Ferreira, J.F., Lobo, J. and Dias, J.
Date: 2015
Divisions: Schools > School of Science and Technology
Record created by: Jonathan Gallacher
Date Added: 04 Apr 2018 15:26
Last Modified: 05 Apr 2018 11:19

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