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.


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


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.


params Get this object’s Parameter types.