Optimal experimental design (OED) seeks experiments expected to yield the most useful
data for some purpose. In practical circumstances where experiments are time-consuming or …

The optimal selection of experimental conditions is essential to maximizing the value of data
for inference and prediction, particularly in situations where experiments are time …

We introduce a fully stochastic gradient based approach to Bayesian optimal experimental
design (BOED). Our approach utilizes variational lower bounds on the expected information …

Experimental design is crucial for inference where limitations in the data collection
procedure are present due to cost or other restrictions. Optimal experimental designs …

We develop a new computational approach for" focused" optimal Bayesian experimental
design with nonlinear models, with the goal of maximizing expected information gain in …

Experimental design in nonlinear settings is complicated by the fact that the efficiency of a
design depends on the unknown parameter values. Thus good designs need to be efficient …

Finding the best setup for experiments is the primary concern for Optimal Experimental
Design (OED). Here, we focus on the Bayesian experimental design problem of finding the …

The optimal design of experiments is crucial for maximizing the information content of data
across a wide-range of experimental goals. This paper presents a Bayesian approach to …

In this paper we propose an efficient stochastic optimization algorithm to search for Bayesian
experimental designs such that the expected information gain is maximized. The gradient of …

Experimentation is key to learning about our world, but careful design of experiments is
critical to ensure resources are used efficiently to conduct discerning investigations …