Struct BayesianUpdate 

pub struct BayesianUpdate;

Conjugate Bayesian updates for standard distribution families.

Each method returns updated (posterior) hyperparameters given a prior and observed data.

Implementations

impl BayesianUpdate

pub fn normal_normal( prior_mean: f64, prior_std: f64, likelihood_std: f64, data: &[f64], ) -> (f64, f64)

Normal-Normal conjugate update: known variance σ², Gaussian prior on μ.

Prior: μ ~ N(μ₀, τ₀²). Data: x_i ~ N(μ, σ²). Returns posterior (μ_n, τ_n).

pub fn beta_bernoulli( prior_alpha: f64, prior_beta: f64, successes: u64, total: u64, ) -> (f64, f64)

Beta-Bernoulli conjugate update: Beta prior on p, Bernoulli observations.

Prior: p ~ Beta(α, β). Data: k successes out of n. Returns posterior (α’, β’).

pub fn gamma_poisson( prior_shape: f64, prior_rate: f64, counts: &[u64], ) -> (f64, f64)

Gamma-Poisson conjugate update: Gamma prior on λ, Poisson observations.

Prior: λ ~ Gamma(α, β). Data: counts k_i. Returns posterior (α’, β’).

pub fn dirichlet_multinomial(prior_alpha: &[f64], counts: &[u64]) -> Vec<f64>

Dirichlet-Multinomial conjugate update.

Prior: p ~ Dir(α). Data: counts k_i. Returns posterior α’ = α + k.

pub fn normal_inverse_gamma( mu0: f64, kappa0: f64, alpha0: f64, beta0: f64, data: &[f64], ) -> (f64, f64, f64, f64)

Normal-inverse-Gamma conjugate update for unknown mean and variance.

Prior hyperparameters: (μ₀, κ₀, α₀, β₀). Returns updated hyperparameters (μ_n, κ_n, α_n, β_n).

Trait Implementations

impl Clone for BayesianUpdate

fn clone(&self) -> BayesianUpdate

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for BayesianUpdate

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

Formats the value using the given formatter.