Package for Priors of Gaussian Processes

This package provides priors for gaussian processes in which you can declare your prior beliefs of the hyperparameter distribution.

Hyperpriors for log likelihood calculation

This module contains a set of commonly used priors for GP models. Note some priors are available in a log transformed space and non-transformed space

pygp.priors.lnpriors.lnFobgp(x, params)

Fobenius norm prior on latent space: x: factors [N x Q] params: scaling parameter params[0]: prior cost Note: this prior only works if the paramter is constraint to be strictly positive

pygp.priors.lnpriors.lnGamma(x, params)

Returns the log gamma (x,k,t) distribution and its derivation with:

lngamma     = (k-1)*log(x) - x/t -gammaln(k) - k*log(t)
dlngamma    = (k-1)/x - 1/t

Parameters:

x : [double]

the interval in which the distribution shall be computed.

params : [k, t]

the distribution parameters k and t.

pygp.priors.lnpriors.lnGammaExp(x, params)

Returns the log gamma (exp(x),k,t) distribution and its derivation with:

lngamma     = (k-1)*log(x) - x/t -gammaln(k) - k*log(t)
dlngamma    = (k-1)/x - 1/t

Parameters:

x : [double]

the interval in which the distribution shall be computed.

params : [k, t]

the distribution parameters k and t.

pygp.priors.lnpriors.lnGauss(x, params)

Returns the log normal distribution and its derivation in interval x, given mean mu and variance sigma:

[N(params), d/dx N(params)] = N(mu,sigma|x).

Note: Give mu and sigma as mean and variance, the result will be logarithmic!

Parameters:

x : [double]

the interval in which the distribution shall be computed.

params : [k, t]

the distribution parameters k and t.

pygp.priors.lnpriors.lnL1(x, params)

L1 type prior defined on the non-log weights params[0]: prior cost Note: this prior only works if the paramter is constraint to be strictly positive

pygp.priors.lnpriors.lnuniformpdf(x, params)

Implementation of lnzeropdf for development purpose only. This pdf returns always [0,0].