Prior classes

The inference.priors module provides tools for constructing prior distributions over the model variables. Example code demonstrating their use can be found in the Gaussian fitting jupyter notebook demo.

GaussianPrior

class inference.priors.GaussianPrior(mean: ndarray, sigma: ndarray, variable_indices: list[int])

A class for generating a Gaussian prior for one or more of the model variables.

Parameters:

mean – The means of the Gaussian priors on each of the variables specified in the variable_indices argument as a 1D numpy.ndarray.
sigma – The standard deviations of the Gaussian priors on each of the variables specified in the variable_indices argument as a 1D numpy.ndarray.
variable_indices – A list of integers specifying the indices of the variables to which the prior will apply.

__call__(theta: ndarray) → float

Returns the prior log-probability value for the provided set of model parameters.

Parameters:: theta – The model parameters as a 1D numpy.ndarray.
Returns:: The prior log-probability value.

gradient(theta: ndarray) → ndarray

Returns the gradient of the prior log-probability with respect to the model parameters.

Parameters:: theta – The model parameters as a 1D numpy.ndarray.
Returns:: The gradient of the prior log-probability with respect to the model parameters.

sample() → ndarray

Draws a sample from the prior.

Returns:: A single sample from the prior distribution as a 1D numpy.ndarray.

UniformPrior

class inference.priors.UniformPrior(lower: ndarray, upper: ndarray, variable_indices: list[int])

A class for generating a uniform prior for one or more of the model variables.

Parameters:

lower – The lower bound of the uniform priors on each of the variables specified in the variable_indices argument as a 1D numpy.ndarray.
upper – The upper bound of the uniform priors on each of the variables specified in the variable_indices argument as a 1D numpy.ndarray.
variable_indices – A list of integers specifying the indices of the variables to which the prior will apply.

__call__(theta: ndarray) → float

Returns the prior log-probability value for the provided set of model parameters.

Parameters:: theta – The model parameters as a 1D numpy.ndarray.
Returns:: The prior log-probability value.

gradient(theta: ndarray) → ndarray

Returns the gradient of the prior log-probability with respect to the model parameters.

Parameters:: theta – The model parameters as a 1D numpy.ndarray.
Returns:: The gradient of the prior log-probability with respect to the model parameters.

sample() → ndarray

Draws a sample from the prior.

Returns:: A single sample from the prior distribution as a 1D numpy.ndarray.

ExponentialPrior

class inference.priors.ExponentialPrior(beta: ndarray, variable_indices: list[int])

A class for generating an exponential prior for one or more of the model variables.

Parameters:

beta – The ‘beta’ parameter values of the exponential priors on each of the variables specified in the variable_indices argument as a 1D numpy.ndarray.
variable_indices – A list of integers specifying the indices of the variables to which the prior will apply.

__call__(theta: ndarray) → float

Returns the prior log-probability value for the provided set of model parameters.

Parameters:: theta – The model parameters as a 1D numpy.ndarray.
Returns:: The prior log-probability value.

gradient(theta: ndarray) → ndarray

Returns the gradient of the prior log-probability with respect to the model parameters.

Parameters:: theta – The model parameters as a 1D numpy.ndarray.
Returns:: The gradient of the prior log-probability with respect to the model parameters.

sample() → ndarray

Draws a sample from the prior.

Returns:: A single sample from the prior distribution as a 1D numpy.ndarray.

JointPrior

class inference.priors.JointPrior(components: list[BasePrior], n_variables: int)

A class which combines multiple prior distribution objects into a single joint-prior distribution object.

Parameters:

components – A list of prior distribution objects (e.g. GaussianPrior, ExponentialPrior) which will be combined into a single joint-prior object.
n_variables (int) – The total number of model variables.

__call__(theta: ndarray) → float

Returns the joint-prior log-probability value, calculated as the sum of the log-probabilities from each prior component for the provided set of model parameters.

Parameters:: theta – The model parameters as a 1D numpy.ndarray.
Returns:: The prior log-probability value.

gradient(theta: ndarray) → ndarray

Returns the gradient of the prior log-probability with respect to the model parameters.

Parameters:: theta – The model parameters as a 1D numpy.ndarray.
Returns:: The gradient of the prior log-probability with respect to the model parameters.

sample() → ndarray

Draws a sample from the prior.

Returns:: A single sample from the prior distribution as a 1D numpy.ndarray.