Struct LikelihoodEvaluator 

pub struct LikelihoodEvaluator { /* private fields */ }

A structure to evaluate and minimize combinations of LikelihoodTerms.

Implementations

impl LikelihoodEvaluator

pub fn minimize( &self, settings: MinimizationSettings<Self>, ) -> Result<MinimizationSummary, LadduError>

Minimize the LikelihoodEvaluator with the algorithm given in the MinimizationSettings.

Errors

This method may return an error if there was any problem constructing thread pools or evaluating the underlying model.

pub fn mcmc( &self, settings: MCMCSettings<Self>, ) -> Result<MCMCSummary, LadduError>

Run an MCMC sampling algorithm over the LikelihoodEvaluator with the given MCMCSettings.

Errors

This method may return an error if there was any problem constructing thread pools or evaluating the underlying model.

Trait Implementations

impl Clone for LikelihoodEvaluator

fn clone(&self) -> LikelihoodEvaluator

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl CostFunction<MaybeThreadPool, LadduError> for LikelihoodEvaluator

fn evaluate( &self, parameters: &DVector<Float>, args: &MaybeThreadPool, ) -> Result<Float, LadduError>

The evaluation of the function at a point x with the given arguments/user data.

impl Gradient<MaybeThreadPool, LadduError> for LikelihoodEvaluator

fn gradient( &self, parameters: &DVector<Float>, args: &MaybeThreadPool, ) -> Result<DVector<Float>, LadduError>

The evaluation of the gradient at a point x with the given arguments/user data.

fn hessian( &self, x: &Matrix<f64, Dyn, Const<1>, VecStorage<f64, Dyn, Const<1>>>, args: &U, ) -> Result<Matrix<f64, Dyn, Dyn, VecStorage<f64, Dyn, Dyn>>, E>

The evaluation of the hessian at a point x with the given arguments/user data.

impl LikelihoodTerm for LikelihoodEvaluator

fn parameters(&self) -> Vec<String>

The parameter names used in LikelihoodEvaluator::evaluate’s input in order.

fn evaluate(&self, parameters: &[Float]) -> Float

A function that can be called to evaluate the sum/product of the LikelihoodTerms contained by this LikelihoodEvaluator.

fn evaluate_gradient(&self, parameters: &[Float]) -> DVector<Float>

Evaluate the gradient of the stored LikelihoodExpression over the events in the Dataset stored by the LikelihoodEvaluator with the given values for free parameters.

fn update(&self)

A method called every step of any minimization/MCMC algorithm.

impl LogDensity<MaybeThreadPool, LadduError> for LikelihoodEvaluator

fn log_density( &self, parameters: &DVector<Float>, args: &MaybeThreadPool, ) -> Result<Float, LadduError>

The log of the evaluation of the density function at a point x with the given arguments/user data.