revrand.likelihoods.Gaussian¶
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class
revrand.likelihoods.Gaussian(var=Parameter(value=1.0, bounds=Positive(upper=None), shape=()))¶ A univariate Gaussian likelihood for general regression tasks.
No transformation function is needed since this is (conditionally) conjugate to the GLM prior.
\[p(y_i | f_i) = \frac{1}{\sqrt{2 \pi \sigma^2}} \exp\left(- \frac{(y_i - f_i)^2}{2 \sigma^2} \right)\]where \(y_i\) is a target, \(f_i\) the value of the latent function corresponding to the target and \(\sigma\) is the observation noise (standard deviation).
Parameters: var (Parameter, optional) – A scalar Parameter describing the initial point and bounds for an optimiser to learn the variance parameter of this object. -
__init__(var=Parameter(value=1.0, bounds=Positive(upper=None), shape=()))¶ See class docstring.
Methods
Ey(f, var)Expected value of the Gaussian likelihood. __init__([var, bounds, shape])See class docstring. cdf(y, f, var)Cumulative density function of the likelihood. df(y, f, var)Derivative of Gaussian log likelihood w.r.t. f. dp(y, f, var)Derivative of Gaussian log likelihood w.r.t.the variance \(\sigma^2\). loglike(y, f[, var])Gaussian log likelihood. Attributes
paramsGet this object’s Parameter types. -
