Trait rv::process::RandomProcess

pub trait RandomProcess<X>
where
    X: Scalar + Debug,
    Self: Sized,
{
    type Error: Error + Send + Sync + 'static;
    type Index;
    type SampleFunction: Rv<DVector<X>>;

    // Required methods
    fn sample_function(&self, indices: &[Self::Index]) -> Self::SampleFunction;
    fn ln_m(&self) -> f64;
    fn ln_m_with_params(
        &self,
        parameter: &DVector<f64>,
    ) -> Result<(f64, DVector<f64>), Self::Error>;
    fn parameters(&self) -> DVector<f64>;
    fn set_parameters(
        self,
        parameters: &DVector<f64>,
    ) -> Result<Self, Self::Error>;
}

A representation of a generic random process

Required Associated Types

type Error: Error + Send + Sync + 'static

Error from

type Index

Type of the indexing set.

type SampleFunction: Rv<DVector<X>>

Type of the sample function, aka trajectory of the process.

Required Methods

fn sample_function(&self, indices: &[Self::Index]) -> Self::SampleFunction

Create a sample function at the indices given.

fn ln_m(&self) -> f64

Compute the log marginal likelihood

fn ln_m_with_params( &self, parameter: &DVector<f64>, ) -> Result<(f64, DVector<f64>), Self::Error>

Compute the log marginal likelihood with an different set of parameters and compute the gradient.

fn parameters(&self) -> DVector<f64>

Get the parameters

fn set_parameters(self, parameters: &DVector<f64>) -> Result<Self, Self::Error>

Set with the given parameters

Object Safety

This trait is not object safe.

Implementors

impl<K> RandomProcess<f64> for GaussianProcess<K>

where K: Kernel,

type Index = Matrix<f64, Dyn, Const<1>, VecStorage<f64, Dyn, Const<1>>>

type SampleFunction = GaussianProcessPrediction<K>

type Error = GaussianProcessError