pybop.costs.log_likelihoods

Classes

GaussianLogLikelihood	This class represents a Gaussian log-likelihood, which evaluates the log-likelihood under
GaussianLogLikelihoodKnownSigma	This class represents a Gaussian log-likelihood with a known sigma, which evaluates the
LogLikelihood	Base class for likelihoods.

Module Contents

class pybop.costs.log_likelihoods.GaussianLogLikelihood(dataset: pybop.processing.dataset.Dataset, sigma: float | list[float] | list[pybop.parameters.parameter.Parameter] = 0.01, target: str | list[str] = None)

Bases: LogLikelihood

This class represents a Gaussian log-likelihood, which evaluates the log-likelihood under the assumption that measurement noise on the target data follows a Gaussian distribution.

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

_logpi

Precomputed offset value for the log-likelihood function.

Type:

float

__call__(r: numpy.ndarray, dy: numpy.ndarray | None = None, inputs: pybop.parameters.parameter.Inputs | None = None) → float | tuple[float, numpy.ndarray]

Compute the Gaussian log-likelihood for the given parameters.

_add_single_sigma(index, value)

_pad_sigma(sigma)

set_sigma(sigma: float | list[float] | list[pybop.parameters.parameter.Parameter])

Set the noise variance (sigma) after checking its validity.

_logpi

class pybop.costs.log_likelihoods.GaussianLogLikelihoodKnownSigma(dataset: pybop.processing.dataset.Dataset, sigma: list[float] | float, target: str | list[str] = None)

Bases: LogLikelihood

This class represents a Gaussian log-likelihood with a known sigma, which evaluates the log-likelihood under the assumption that measurement noise on the target data follows a Gaussian distribution.

Parameters:

sigma (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.

__call__(r: numpy.ndarray, dy: numpy.ndarray | None = None, inputs: pybop.parameters.parameter.Inputs | None = None) → float | tuple[float, numpy.ndarray]

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

set_sigma(sigma: numpy.ndarray | float)

Set sigma after checking its validity.

class pybop.costs.log_likelihoods.LogLikelihood(dataset: pybop.processing.dataset.Dataset, target: str | list[str] = None)

Bases: pybop.costs.error_measures.ErrorMeasure

Base class for likelihoods.

Exists to distinguish between error measures and likelihood-based costs.

abstractmethod set_sigma(sigma: numpy.ndarray | float, n_outputs: int, n_data: int)

Set the noise variance (sigma) after checking its validity.

minimising = False

parameters

sigma = None