# entropyData: Computes an Empirical Estimation of the Entropy from a Table... In abn: Modelling Multivariate Data with Additive Bayesian Networks

 entropyData R Documentation

## Computes an Empirical Estimation of the Entropy from a Table of Counts

### Description

This function empirically estimates the Shannon entropy from a table of counts using the observed frequencies.

### Usage

```entropyData(freqs.table)
```

### Arguments

 `freqs.table` a table of counts.

### Details

The general concept of entropy is defined for probability distributions. The `entropyData` function estimates empirical entropy from data. The probability is estimated from data using frequency tables. Then the estimates are plug-in in the definition of the entropy to return the so-called empirical entropy. A common known problem of empirical entropy is that the estimations are biased due to the sampling noise. This is also known that the bias will decrease as the sample size increases.

### Value

Shannon's entropy estimate on natural logarithm scale.

Gilles Kratzer

### References

Cover, Thomas M, and Joy A Thomas. (2012). "Elements of Information Theory". John Wiley & Sons.

`discretization`

### Examples

```## Generate random variable
rv <- rnorm(n = 100, mean = 0, sd = 2)
dist <- list("gaussian")
names(dist) <- c("rv")

## Compute the entropy through discretization
entropyData(discretization(data.df = rv, data.dists = dist,
discretization.method = "fd", nb.states = FALSE))
```

abn documentation built on April 25, 2022, 9:06 a.m.