Publication

Anthropic Correction of Information Estimates

Michael Christoph Gastpar
2009
Conference paper

Abstract

A novel estimator for mutual information is proposed. The estimator is useful for the (asymmetric) scenario where only a few samples for one random variable are available, but for each sample, the conditional distribution of the other random variable can be accurately characterized. Such asymmetry is common in neuroscience where it is often necessary to repeat the same stimulus many times to obtain a stable response.

Official source

https://infoscience.epfl.ch/record/169537?ln=en

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Anthropic Correction of Information Estimates

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Information Spectrum Converse for Minimum Entropy Couplings and Functional Representations

Diffusion in Style

Information Spectrum Converse for Minimum Entropy Couplings and Functional Representations

Diffusion in Style