# revrand.likelihoods.Binomial¶

class revrand.likelihoods.Binomial

Binomial likelihood class.

A logistic transformation function is used to map the latent function from the GLM prior into a probability.

$p(y_i | f_i) = \genfrac(){0pt}{}{n}{y_i} \sigma(f_i) ^ {y_i} (1 - \sigma(f_i))^{n - y_i}$

where $$y_i$$ is a target, $$f_i$$ the value of the latent function corresponding to the target, $$n$$ is the total possible count, and $$\sigma(\cdot)$$ is the logistic sigmoid. $$n$$ can also be applied per observation.

__init__()

Initialize self. See help(type(self)) for accurate signature.

Methods

 Ey(f, n) Expected value of the Binomial likelihood. __init__ Initialize self. cdf(y, f, n) Cumulative density function of the likelihood. df(y, f, n) Derivative of Binomial log likelihood w.r.t. f. dp(y, f, *args) Derivative of Bernoulli log likelihood w.r.t.the parameters, $$\theta$$. loglike(y, f, n) Binomial log likelihood.

Attributes

 params Get this object’s Parameter types.