Sequential optimal design of neurophysiology experiments
Neural Computation
Monte Carlo Statistical Methods
Monte Carlo Statistical Methods
Computational Statistics & Data Analysis
Optimal sequential designs in phase I studies
Computational Statistics & Data Analysis
Bayesian D-optimal designs for the two parameter logistic mixed effects model
Computational Statistics & Data Analysis
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In this paper we present a sequential Monte Carlo algorithm for Bayesian sequential experimental design applied to generalised non-linear models for discrete data. The approach is computationally convenient in that the information of newly observed data can be incorporated through a simple re-weighting step. We also consider a flexible parametric model for the stimulus-response relationship together with a newly developed hybrid design utility that can produce more robust estimates of the target stimulus in the presence of substantial model and parameter uncertainty. The algorithm is applied to hypothetical clinical trial or bioassay scenarios. In the discussion, potential generalisations of the algorithm are suggested to possibly extend its applicability to a wide variety of scenarios.