3 accepted papers
Bayesian Experimental Design (BED), which aims to find the optimal experimental conditions for Bayesian inference, is usually posed as to optimize the expected information gain (EIG). The gradient information is often needed for efficient EIG optimization, and as a result the ability to estimate the…
Entropy estimation is an important problem in information theory and statistical science. Many popular entropy estimators suffer from fast growing estimation bias with respect to dimensionality, rendering them unsuitable for high dimensional problems. In this work we propose a transformbased method…
Data collection is a critical step in statistical inference and data science,and the goal of statistical experimental design (ED) is to find the data collection setupthat can provide most information for the inference. In this work we consider a special type of ED problems where the likelihoods are…