Struct VariationalPosterior 

pub struct VariationalPosterior {
    pub weight_mean: Array1<Float>,
    pub weight_covariance: Array2<Float>,
    pub weight_precision: Array2<Float>,
    pub weight_precision_shape: Array1<Float>,
    pub weight_precision_rate: Array1<Float>,
    pub noise_precision_shape: Float,
    pub noise_precision_rate: Float,
    pub elbo: Float,
}

Variational parameters for the posterior distribution

Fields

weight_mean: Array1<Float>

Mean of weight posterior (multivariate normal)

weight_covariance: Array2<Float>

Covariance of weight posterior

weight_precision: Array2<Float>

Precision matrix (inverse covariance)

weight_precision_shape: Array1<Float>

Parameters for weight precision posterior (Gamma)

weight_precision_rate: Array1<Float>

noise_precision_shape: Float

Parameters for noise precision posterior (Gamma)

noise_precision_rate: Float

elbo: Float

Log marginal likelihood lower bound (ELBO)

Implementations

impl VariationalPosterior

pub fn new(n_features: usize, config: &PriorConfiguration) -> Self

Create a new variational posterior with given dimensions

pub fn sample_weights( &self, n_samples: usize, rng: &mut impl Rng, ) -> Result<Array2<Float>>

Sample from the posterior distribution

Trait Implementations

impl Clone for VariationalPosterior

fn clone(&self) -> VariationalPosterior

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for VariationalPosterior

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.