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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

    Typeint

    Default1

    Initial alpha parameter.

  • beta

    Typeint

    Default1

    Initial beta parameter.

  • seed

    Typeint | None

    DefaultNone

    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...)

beta.cdf(.35)
0.994168...

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'