Experimentation is at the core of research in the behavioral and neural sciences, yet
observations can be expensive and time-consuming to acquire (eg, MRI scans, responses …

Experimentation is ubiquitous in the field of psychology and fundamental to the
advancement of its science, and one of the biggest challenges for researchers is designing …

Experimental design is fundamental to research, but formal methods to identify good
designs are lacking. Advances in Bayesian statistics and machine learning offer algorithm …

Bayesian optimisation is a statistical method that efficiently models and optimises expensive
“black-box” functions. This review considers the application of Bayesian optimisation to …

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

Discriminating among competing statistical models is a pressing issue for many
experimentalists in the field of cognitive science. Resolving this issue begins with designing …

Suppose an online platform wants to compare a treatment and control policy (eg, two
different matching algorithms in a ridesharing system, or two different inventory management …

Bayesian optimal experimental design (BOED) is a principled framework for making efficient
use of limited experimental resources. Unfortunately, its applicability is hampered by the …

We describe the R package acebayes and demonstrate its use to find Bayesian optimal
experimental designs. A decision-theoretic approach is adopted, with the optimal design …

Cognitive neuroscientists are often interested in broad research questions, yet use overly
narrow experimental designs by considering only a small subset of possible experimental …