The long tail in Bayesian optimal control in uncertain environments

Darwen, Paul J. (2010) The long tail in Bayesian optimal control in uncertain environments. In: Proceedings of 2010 Second World Congress on Nature and Biologically Inspired Computing, pp. 579-586. From: 2010 Second World Congress on Nature and Biologically Inspired Computing, 15-17 December 2010, Fukuoka, Japan.

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Abstract

Evolutionary algorithms and other biologically-inspired approaches to optimization rely on models that can be simulated in software. When calibrating a model to noisy and insufficient data, the single best-fitting model is often used. In contrast, Bayesian model averaging is known to give a better handle on uncertainty, but at the price of vastly more computation. This paper asks how much better, at how much more computation. The example problem uses the Bates model of stock price behaviour applied to barrier options, a problem similar to risk management with rainfall models.

Item ID: 22362
Item Type: Conference Item (Refereed Research Paper - E1)
Keywords: bayesian posterior distribution; model averaging; option price models; rainfall models; risk management
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ISBN: 978-1-4244-7375-5
Date Deposited: 22 May 2013 02:09
FoR Codes: 08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080108 Neural, Evolutionary and Fuzzy Computation @ 80%
08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080110 Simulation and Modelling @ 20%
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