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.