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Activity Number:
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125
- Bayesian Methods for Discrete Data Problems
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Type:
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Contributed
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Date/Time:
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Monday, July 31, 2017 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Bayesian Statistical Science
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Abstract #324276
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Title:
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Dual-Purpose Bayesian Design for Parameter Estimation and Model Discrimination of Models with Intractable Likelihoods
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Author(s):
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Mahasen Bandara Dehideniya* and James McGree and Christopher Drovandi
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Companies:
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Queensland University of Technology and Queensland University of Technology and Queensland University of Technology
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Keywords:
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Kullback-Leibler divergence ;
Mutual Information ;
Synthetic likelihood ;
Total entropy
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Abstract:
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In this work, we propose a methodology based on total entropy to design dual-purpose experiments of parameter estimation and model discrimination for epidemiological models with computationally intractable likelihoods. Our methodology uses a novel synthetic likelihood approach to approximate the likelihood of discrete observations of a stochastic process and to approximate the total entropy utility function. We consider four stochastic processes namely the death model, the Susceptible-Infected model, the Susceptible-Infected-Recovered model, and the Susceptible-Exposed-Infected-Recovered. The model discrimination and parameter estimation properties of the designs were compared against optimal choices under the mutual information utility for model discrimination and the Kullback-Leibler divergence utility for parameter estimation. The results suggest that these dual-purpose designs perform efficiently under both experimental goals.
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Authors who are presenting talks have a * after their name.
