Entropy estimation

<p>In various science/engineering applications, such as <a href="/facts/Independent_component_analysis/gsdVaDFR">independent component analysis</a>, <a href="/facts/Image_analysis/woLB8cX7">image analysis</a>, <a href="/facts/Genetic_analysis/mBppwMUv">genetic analysis</a>, <a href="/facts/Speech_recognition/z7S7Pgk6">speech recognition</a>, <a href="/facts/Manifold_learning/1lL1Y0vN">manifold learning</a>, and time delay estimation it is useful to estimate the <a href="/facts/Differential_entropy/EoJSyw95">differential entropy</a> of a system or process, given some observations.
</p><p>The simplest and most common approach uses <a href="/facts/Histogram/oMmvqGEA">histogram</a>-based estimation, but other approaches have been developed and used, each with its own benefits and drawbacks. The main factor in choosing a method is often  a trade-off between the bias and the variance of the estimate, although the nature of the (suspected) distribution of the data may also be a factor, as well as the sample size and the size of the alphabet of the probability distribution. 
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Entropy estimation open-in-new

Entropy estimation