Sequential Optimal Inference for Experiments With Bayesian Particle Filters

30 Pages Posted: 9 Feb 2019

See all articles by Remi Daviet

Remi Daviet

University of Pennsylvania - Marketing Department

Date Written: January 21, 2019

Abstract

In behavioral experiments, carefully choosing the stimuli is critical for success. Recently, several "adaptive" Bayesian methods gained popularity by proposing to optimally select the stimulus in each trial based on the results of the preceding trials. However, current methods are computationally expensive and might require a long waiting period between each question. Moreover, they are often tailored to a particular model and a particular objective, such as parameter estimation, prediction or model selection. It is left to the researcher to extend these approaches to other models by providing a suitable Bayesian inference method. We propose to apply the Sequential Monte Carlo (SMC) framework to solve both the inference problem and the optimal experimental design problem. This new method, called Sequential Optimal Inference (SOI) provides gains in computational efficiency and allows for the use of a broad class of complex models and objectives. We demonstrate its validity with simulation studies. An implementation of the method in MATLAB and Python is provided.

Keywords: Optimal Design, Adaptive Design, Experimental Design

Suggested Citation

Daviet, Remi, Sequential Optimal Inference for Experiments With Bayesian Particle Filters (January 21, 2019). Available at SSRN: https://ssrn.com/abstract=3320251 or http://dx.doi.org/10.2139/ssrn.3320251

Remi Daviet (Contact Author)

University of Pennsylvania - Marketing Department ( email )

700 Jon M. Huntsman Hall
3730 Walnut Street
Philadelphia, PA 19104-6340
United States

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