Entropy-Based Search Algorithm for Experimental Design

TL;DR

This paper introduces a nested entropy sampling algorithm for efficient experimental design, optimizing the selection of informative experiments in high-dimensional spaces by approximating maximum entropy, thus improving over brute force methods.

Contribution

The paper presents a novel nested entropy sampling algorithm inspired by nested sampling, enabling efficient high-dimensional entropy-based experiment selection.

Findings

The algorithm effectively identifies highly informative experiments.

It outperforms brute force search in computational efficiency.

Demonstrates potential for autonomous experimental design.

Abstract

The scientific method relies on the iterated processes of inference and inquiry. The inference phase consists of selecting the most probable models based on the available data; whereas the inquiry phase consists of using what is known about the models to select the most relevant experiment. Optimizing inquiry involves searching the parameterized space of experiments to select the experiment that promises, on average, to be maximally informative. In the case where it is important to learn about each of the model parameters, the relevance of an experiment is quantified by Shannon entropy of the distribution of experimental outcomes predicted by a probable set of models. If the set of potential experiments is described by many parameters, we must search this high-dimensional entropy space. Brute force search methods will be slow and computationally expensive. We present an entropy-based search algorithm, called nested entropy sampling, to select the most informative experiment for efficient experimental design. This algorithm is inspired by Skilling's nested sampling algorithm used in inference and borrows the concept of a rising threshold while a set of experiment samples are maintained. We demonstrate that this algorithm not only selects highly relevant experiments, but also is more efficient than brute force search. Such entropic search techniques promise to greatly benefit autonomous experimental design.