UCL Discovery Stage
UCL home » Library Services » Electronic resources » UCL Discovery Stage

Active Data Selection and Information Seeking

Parr, Thomas; Friston, Karl; Zeidman, Peter; (2024) Active Data Selection and Information Seeking. Algorithms , 17 (3) , Article 118. 10.3390/a17030118. Green open access

[thumbnail of algorithms-17-00118-v2.pdf]
Preview
PDF
algorithms-17-00118-v2.pdf - Published Version

Download (5MB) | Preview

Abstract

Bayesian inference typically focuses upon two issues. The first is estimating the parameters of some model from data, and the second is quantifying the evidence for alternative hypotheses—formulated as alternative models. This paper focuses upon a third issue. Our interest is in the selection of data—either through sampling subsets of data from a large dataset or through optimising experimental design—based upon the models we have of how those data are generated. Optimising data-selection ensures we can achieve good inference with fewer data, saving on computational and experimental costs. This paper aims to unpack the principles of active sampling of data by drawing from neurobiological research on animal exploration and from the theory of optimal experimental design. We offer an overview of the salient points from these fields and illustrate their application in simple toy examples, ranging from function approximation with basis sets to inference about processes that evolve over time. Finally, we consider how this approach to data selection could be applied to the design of (Bayes-adaptive) clinical trials.

Type: Article
Title: Active Data Selection and Information Seeking
Open access status: An open access version is available from UCL Discovery
DOI: 10.3390/a17030118
Publisher version: https://doi.org/10.3390/a17030118
Language: English
Additional information: : © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).
Keywords: experimental design; active sampling; information gain; Bayesian inference
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Imaging Neuroscience
URI: https://discovery-pp.ucl.ac.uk/id/eprint/10190280
Downloads since deposit
410Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item