Prior distribution¶

We support Gaussian prior distributions, specifically IsotropicGaussianPrior and DiagonalGaussianPrior.

Alternatively, you can bring your own prior distribution by overwriting the abstract class Prior. Please find the references below.

class fortuna.prob_model.prior.base.Prior[source]¶

Abstract prior distribution class.

abstract log_joint_prob(params)[source]¶

Evaluate the prior log-probability density function (a.k.a. log-pdf).

Parameters:: params (PyTree) – The parameters where to evaluate the log-pdf.
Returns:: Evaluation of the prior log-pdf.
Return type:: float

property rng: RandomNumberGenerator¶

Invoke the random number generator object.

Return type:: The random number generator object.

abstract sample(params_like, rng=None)[source]¶

Sample parameters from the prior distribution.

Parameters:

params_like (PyTree) – An PyTree object with the same structure as the parameters to sample.
rng (Optional[jax.Array]) – A random number generator. If not passed, this will be taken from the attributes of this class.

Returns:

A sample from the prior distribution.

Return type:

PyTree

class fortuna.prob_model.prior.gaussian.DiagonalGaussianPrior(log_var)[source]¶

A diagonal Gaussian prior class.

Parameters:: log_var (jnp.ndarray) – Prior log-variance vector corresponding to the logarithm of the diagonal of the prior covariance matrix.

log_joint_prob(params)[source]¶

Evaluate the prior log-probability density function (a.k.a. log-pdf).

Parameters:: params (PyTree) – The parameters where to evaluate the log-pdf.
Returns:: Evaluation of the prior log-pdf.
Return type:: float

property rng: RandomNumberGenerator¶

Invoke the random number generator object.

Return type:: The random number generator object.

sample(params_like, rng=None)[source]¶

Sample parameters from the prior distribution.

Parameters:

params_like (PyTree) – An PyTree object with the same structure as the parameters to sample.
rng (Optional[jax.Array]) – A random number generator. If not passed, this will be taken from the attributes of this class.

Returns:

A sample from the prior distribution.

Return type:

PyTree

class fortuna.prob_model.prior.gaussian.IsotropicGaussianPrior(log_var=0.0)[source]¶

A diagonal Gaussian prior class.

Parameters:: log_var (Optional[float]) – Prior log-variance value. The covariance matrix of the prior distribution is given by a diagonal matrix with this parameter on every entry of the diagonal.

log_joint_prob(params)[source]¶

Evaluate the prior log-probability density function (a.k.a. log-pdf).

Parameters:: params (PyTree) – The parameters where to evaluate the log-pdf.
Returns:: Evaluation of the prior log-pdf.
Return type:: float

property rng: RandomNumberGenerator¶

Invoke the random number generator object.

Return type:: The random number generator object.

sample(params_like, rng=None)[source]¶

Sample parameters from the prior distribution.

Parameters:

params_like (PyTree) – An PyTree object with the same structure as the parameters to sample.
rng (Optional[jax.Array]) – A random number generator. If not passed, this will be taken from the attributes of this class.

Returns:

A sample from the prior distribution.

Return type:

PyTree