pybop.costs._likelihoods#

Classes#

`BaseLikelihood`	Base class for likelihoods
`GaussianLogLikelihood`	This class represents a Gaussian Log Likelihood, which assumes that the
`GaussianLogLikelihoodKnownSigma`	This class represents a Gaussian Log Likelihood with a known sigma,
`LogPosterior`	The Log Posterior for a given problem.

Module Contents#

class pybop.costs._likelihoods.BaseLikelihood(problem: pybop.problems.base_problem.BaseProblem)[source]#

Bases: pybop.costs.base_cost.BaseCost

Base class for likelihoods

n_data[source]#

class pybop.costs._likelihoods.GaussianLogLikelihood(problem: pybop.problems.base_problem.BaseProblem, sigma0: float | list[float] | list[pybop.parameters.parameter.Parameter] = 0.01, dsigma_scale: float = 1.0)[source]#

Bases: BaseLikelihood

This class represents a Gaussian Log Likelihood, which assumes that the data follows a Gaussian distribution and computes the log-likelihood of observed data under this assumption.

This class estimates the standard deviation of the Gaussian distribution alongside the parameters of the model.

_logpi[source]#

Precomputed offset value for the log-likelihood function.

Type:: float

_dsigma_scale[source]#

Scale factor for derivative of standard deviation.

Type:: float

_add_sigma_parameters(sigma0)[source]#

_add_single_sigma(index, value)[source]#

_pad_sigma0(sigma0)[source]#

compute(y: dict, dy: numpy.ndarray = None, calculate_grad: bool = False) → float | tuple[float, numpy.ndarray][source]#

Compute the Gaussian log-likelihood for the given parameters.

This method only computes the likelihood, without calling problem.evaluate().

Returns:: The log-likelihood value, or -inf if the standard deviations are non-positive.
Return type:: float

_dsigma_scale[source]#

_logpi[source]#

property dsigma_scale[source]#
Scaling factor for the dsigma term in the gradient calculation.

sigma[source]#

class pybop.costs._likelihoods.GaussianLogLikelihoodKnownSigma(problem: pybop.problems.base_problem.BaseProblem, sigma0: list[float] | float)[source]#

Bases: BaseLikelihood

This class represents a Gaussian Log Likelihood with a known sigma, which assumes that the data follows a Gaussian distribution and computes the log-likelihood of observed data under this assumption.

Parameters:: sigma0 (scalar or array) – Initial standard deviation around x0. Either a scalar value (one standard deviation for all coordinates) or an array with one entry per dimension.

check_sigma0(sigma0: numpy.ndarray | float)[source]#: Check the validity of sigma0.

compute(y: dict, dy: numpy.ndarray = None, calculate_grad: bool = False) → float | tuple[float, numpy.ndarray][source]#

Compute the Gaussian log-likelihood for the given parameters with known sigma.

This method only computes the likelihood, without calling the problem.evaluateS1.

_multip[source]#

_offset[source]#

sigma0[source]#

sigma2[source]#

class pybop.costs._likelihoods.LogPosterior(log_likelihood: BaseLikelihood, log_prior: pybop.BasePrior | scipy.stats.rv_continuous | None = None, gradient_step: float = 0.001)[source]#

Bases: BaseLikelihood

The Log Posterior for a given problem.

Computes the log posterior which is proportional to the sum of the log likelihood and the log prior.

Parameters:

log_likelihood (BaseLikelihood) – The likelihood class of type BaseLikelihood.
log_prior (Optional, Union[pybop.BasePrior, stats.rv_continuous]) – The prior class of type BasePrior or stats.rv_continuous. If not provided, the prior class will be taken from the parameter priors constructed in the pybop.Parameters class.
gradient_step (float, default: 1e-3) – The step size for the finite-difference gradient calculation if the log_prior is not of type BasePrior.

compute(y: dict, dy: numpy.ndarray = None, calculate_grad: bool = False) → float | tuple[float, numpy.ndarray][source]#

Calculate the posterior cost for a given forward model prediction.

Parameters:

y (dict) – The data for which to evaluate the cost.
dy (np.ndarray, optional) – The correspond sensitivities in the data.
calculate_grad (bool, optional) – Whether to calculate the gradient of the cost function.

Returns:

The posterior cost, and optionally the gradient.

Return type:

Union[float, Tuple[float, np.ndarray]]

_has_separable_problem[source]#

_log_likelihood[source]#

gradient_step[source]#

property likelihood: BaseLikelihood[source]#

parameters[source]#

property prior: pybop.parameters.priors.BasePrior[source]#