Struct Posterior 

pub struct Posterior {
    pub delta_post: Vector9,
    pub lambda_post: Matrix9,
    pub delta_draws: Vec<Vector9>,
    pub leak_probability: f64,
    pub theta: f64,
    pub n: usize,
    pub lambda_mean: Option<f64>,
    pub lambda_mixing_ok: Option<bool>,
    pub kappa_mean: Option<f64>,
    pub kappa_cv: Option<f64>,
    pub kappa_ess: Option<f64>,
    pub kappa_mixing_ok: Option<bool>,
}

Posterior distribution parameters for the 9D effect vector δ.

The posterior is Gaussian: δ | Δ ~ N(δ_post, Λ_post) where each δ_k represents the timing difference at decile k.

Uses Student’s t prior (ν=4) via Gibbs sampling for robust inference.

Fields

delta_post: Vector9

9D posterior mean δ_post in nanoseconds.

lambda_post: Matrix9

9D posterior covariance Λ_post.

delta_draws: Vec<Vector9>

Retained δ draws from the Gibbs sampler. Used for effect estimation via compute_effect_estimate.

leak_probability: f64

Leak probability: P(max_k |δ_k| > θ | Δ). Computed via Monte Carlo integration over the 9D posterior.

theta: f64

Effect threshold used for leak probability computation.

n: usize

Number of samples used in this posterior computation.

lambda_mean: Option<f64>

Posterior mean of latent scale λ. None if using simple posterior (no Gibbs sampler).

lambda_mixing_ok: Option<bool>

Whether the Gibbs sampler’s lambda chain mixed well. None if using simple posterior. When Some(false), indicates potential posterior unreliability.

kappa_mean: Option<f64>

Posterior mean of likelihood precision κ. None if using simple posterior.

kappa_cv: Option<f64>

Coefficient of variation of κ. None if using simple posterior.

kappa_ess: Option<f64>

Effective sample size of κ chain. None if using simple posterior.

kappa_mixing_ok: Option<bool>

Whether the Gibbs sampler’s kappa chain mixed well. None if using simple posterior.

Implementations

impl Posterior

pub fn new( delta_post: Vector9, lambda_post: Matrix9, delta_draws: Vec<Vector9>, leak_probability: f64, theta: f64, n: usize, ) -> Self

Create a new posterior with given parameters.

pub fn new_with_gibbs( delta_post: Vector9, lambda_post: Matrix9, delta_draws: Vec<Vector9>, leak_probability: f64, theta: f64, n: usize, lambda_mean: f64, lambda_mixing_ok: bool, kappa_mean: f64, kappa_cv: f64, kappa_ess: f64, kappa_mixing_ok: bool, ) -> Self

Create a new posterior with Gibbs sampler diagnostics (v5.4, v5.6).

pub fn max_effect_ns(&self) -> f64

Get the max absolute effect across all deciles from posterior mean.

pub fn to_effect_estimate(&self) -> EffectEstimate

Build an EffectEstimate from this posterior.

Uses delta draws if available, otherwise uses analytical approximation.

pub fn measurement_quality(&self) -> MeasurementQuality

Get measurement quality based on the posterior uncertainty.

Quality is determined by the minimum detectable effect (MDE), which is approximately the maximum marginal standard deviation.

pub fn to_summary(&self) -> PosteriorSummary

Convert to an FFI-friendly summary containing only scalar fields.

Trait Implementations

impl Clone for Posterior

fn clone(&self) -> Posterior

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for Posterior

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

Formats the value using the given formatter.