Related Experiment Video
Updated: Jun 25, 2025

P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation
Published on: September 8, 2023
A Comparative Analysis of Discrete Entropy Estimators for Large-Alphabet Problems
Assaf Pinchas1, Irad Ben-Gal2, Amichai Painsky2
1School of Electrical Engineering, The Iby and Aladar Fleischman Faculty of Engineering, Tel Aviv University, Tel Aviv 6997801, Israel.
This study compares 21 entropy estimators for large alphabets, finding no single best method. Performance depends on data distribution, guiding practical estimator selection.
Area of Science:
- Information Theory
- Machine Learning
- Statistical Inference
Background:
- Numerous entropy estimators exist, each suited for specific data characteristics.
- No single entropy estimator demonstrates universal superiority across all scenarios.
- Large-alphabet entropy estimation presents unique challenges and requires careful method selection.
Purpose of the Study:
- To conduct a comprehensive comparative analysis of twenty-one entropy estimators.
- To evaluate estimator performance across various data distributions in a large-alphabet setting.
- To provide data-driven recommendations for optimal entropy estimation.
Main Methods:
- Comparative evaluation of twenty-one distinct entropy estimation techniques.
- Categorization of underlying data distributions into three classes (uniform to degenerate).
- Development and assessment of a sample-dependent approach for estimator selection.
Main Results:
- Entropy estimator performance is highly contingent on the underlying data distribution.
- Specific estimators are recommended for different distribution classes.
- A sample-dependent framework identifies top-performing estimators tailored to data characteristics.
Conclusions:
- The choice of entropy estimator significantly impacts accuracy in large-alphabet settings.
- Distribution-specific and sample-dependent approaches enhance practical entropy estimation.
- This work offers a valuable guide for selecting appropriate entropy estimators in real-world applications.
More Related Videos
11:15Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
Published on: June 27, 2013
09:23Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans
Published on: August 16, 2017
Related Concept Videos
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
Statistical Analysis: Overview
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
Distributions to Estimate Population Parameter
Random Variables
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
For example, let X = the...
Censoring Survival Data
Sign Test for Matched Pairs
To conduct the sign test, we first calculate the differences in...