kullback_leibler_divergence {abdiv} | R Documentation |

## Kullback-Leibler divergence

### Description

Kullback-Leibler divergence

### Usage

```
kullback_leibler_divergence(x, y)
```

### Arguments

`x` , `y` |
Numeric vectors representing probabilities |

### Details

Kullback-Leibler divergence is a non-symmetric measure of difference between two probability vectors. In general, KL(x, y) is not equal to KL(y, x).

Because this measure is defined for probabilities, the vectors x and y are normalized in the function so they sum to 1.

### Value

The Kullback-Leibler divergence between `x`

and `y`

. We
adopt the following conventions if elements of `x`

or `y`

are
zero: `0 \log (0 / y_i) = 0`

, `0 \log (0 / 0) = 0`

, and
`x_i \log (x_i / 0) = \infty`

. As a result, if elements of `x`

are
zero, they do not contribute to the sum. If elements of `y`

are zero
where `x`

is nonzero, the result will be `Inf`

. If either
`x`

or `y`

sum to zero, we are not able to compute the
proportions, and we return `NaN`

.

*abdiv*version 0.2.0 Index]