# Beta¶

Beta distribution for binary data.

A Beta distribution is very similar to a Bernoulli distribution in that it counts occurrences of boolean events. The differences lies in what is being measured. A Binomial distribution models the probability of an event occurring, whereas a Beta distribution models the probability distribution itself. In other words, it's a probability distribution over probability distributions.

## Parameters¶

• alpha (int) – defaults to 1

Initial alpha parameter.

• beta (int) – defaults to 1

Initial beta parameter.

• seed (int) – defaults to None

Random number generator seed for reproducibility.

## Attributes¶

• mode

The most likely value in the distribution.

• n_samples

The number of observed samples.

## Examples¶

>>> from river import proba

>>> successes = 81
>>> failures = 219
>>> beta = proba.Beta(successes, failures)

>>> beta(.21), beta(.35)
(0.867..., 0.165...)

>>> for success in range(100):
...     beta = beta.update(True)
>>> for failure in range(200):
...     beta = beta.update(False)

>>> beta(.21), beta(.35)
(2.525...e-05, 0.841...)


## Methods¶

call

Probability mass/density function.

Parameters

• p (float)
cdf

Cumulative density function, i.e. P(X <= x).

Parameters

• x (float)
revert

Reverts the parameters of the distribution for a given observation.

Parameters

• x (float)
sample

Sample a random value from the distribution.

update

Updates the parameters of the distribution given a new observation.

Parameters

• x (float)