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An intuition for physicists: information gain from experiments

Authors :
Johannes Buchner
Publication Year :
2022
Publisher :
arXiv, 2022.

Abstract

How much one has learned from an experiment is quantifiable by the information gain, also known as the Kullback-Leibler divergence. The narrowing of the posterior parameter distribution $P(\theta|D)$ compared with the prior parameter distribution $\pi(\theta)$, is quantified in units of bits, as: $ D_{\mathrm{KL}}(P|\pi)=\int\log_{2}\left(\frac{P(\theta|D)}{\pi(\theta)}\right)\,P(\theta|D)\,d\theta $. This research note gives an intuition what one bit of information gain means. It corresponds to a Gaussian shrinking its standard deviation by a factor of three.<br />Comment: Accepted to RNAAS; Corrected typos (Thanks to Tariq Yasin and Torsten En{\ss}lin)

Details

Database :
OpenAIRE
Accession number :
edsair.doi.dedup.....e1e34b14a604f1de6a0d8a15a8943c45
Full Text :
https://doi.org/10.48550/arxiv.2205.00009