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, sigma, variable_indices)
A class for generating a Gaussian prior for one or more of the model variables.
- Parameters
mean – A list specifying the means of the Gaussian priors on each of the variables specified in the
variable_indicesargument.sigma – A list specifying the standard deviations of the Gaussian priors on each of the variables specified in the
variable_indicesargument.variable_indices – A list of integers specifying the indices of the variables to which the prior will apply.
- __call__(theta)
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)
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()
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, upper, variable_indices)
A class for generating a uniform prior for one or more of the model variables.
- Parameters
lower – A list specifying the lower bound of the uniform priors on each of the variables specified in the
variable_indicesargument.upper – A list specifying the upper bound of the uniform priors on each of the variables specified in the
variable_indicesargument.variable_indices – A list of integers specifying the indices of the variables to which the prior will apply.
- __call__(theta)
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)
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()
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, variable_indices)
A class for generating an exponential prior for one or more of the model variables.
- Parameters
beta – A list specifying the ‘beta’ parameter value of the exponential priors on each of the variables specified in the
variable_indicesargument.variable_indices – A list of integers specifying the indices of the variables to which the prior will apply.
- __call__(theta)
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)
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()
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, n_variables)
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)
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)
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()
Draws a sample from the prior.
- Returns
A single sample from the prior distribution as a 1D
numpy.ndarray.