Using Particle Filters to Analyse the Credibility in Model Predictions

Abstract:

Models are often used to make predictions far from the region where they were trained and validated. In this paper attempts are made to analyse the credibility that can be placed in such predictions. The proposed approach involves treating a model’s parameters as time-variant (even if it is believed that this is not the case), before utilising Bayesian tracking techniques to realise parameter estimates. An example is used to demonstrate that, relative to a Bayesian approach where the parameters are assumed to be time-invariant, treating the parameters as time-variant can reveal important flaws in the model and raise questions about its ability to make credible predictions.

• Periodical: Applied Mechanics and Materials (Volume 807)
• Pages: 218-225
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.807.218
• Online since: November 2015
• Authors: Peter Lewis Green*
• Keywords: Bayesian Inference, Model Predictions, Nonlinear System Identification, Particle Filters

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