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Activity Number:
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573
- Design of Experiments for Stochastic Process Models
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Type:
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Topic Contributed
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Date/Time:
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Wednesday, August 1, 2018 : 2:00 PM to 3:50 PM
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Sponsor:
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Royal Statistical Society
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Abstract #329265
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Presentation
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Title:
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Optimal Bayesian Design for Models with Intractable Likelihoods via Machine Learning Methods
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Author(s):
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Christopher C Drovandi and Markus Hainy*
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Companies:
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Queensland University of Technology and QUT
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Keywords:
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experimental design;
Bayesian;
model selection;
machine learning;
intractable likelihoods;
approximate Bayesian computation
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Abstract:
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Optimal Bayesian experimental design is often computationally intensive due to the need to approximate many posterior distributions for datasets simulated from the prior predictive distribution. The issues are compounded further when the statistical models of interest do not possess tractable likelihood functions and only simulation is feasible. We adopt machine learning methods like trees and random forests to facilitate the computation of utility values in optimal Bayesian design when the experimental goal is to discriminate between different models.
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Authors who are presenting talks have a * after their name.
