Struct rv::dist::NormalGamma

pub struct NormalGamma {
    pub m: f64,
    pub r: f64,
    pub s: f64,
    pub v: f64,
}

Prior for Gaussian

Given x ~ N(μ, σ), the Normal Gamma prior implies that μ ~ N(m, 1/(rρ)) and ρ ~ Gamma(ν/2, s/2).

Fields

m: f64

r: f64

s: f64

v: f64

Methods

impl NormalGamma

pub fn new(m: f64, r: f64, s: f64, v: f64) -> Result<Self>

Trait Implementations

impl Debug for NormalGamma

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

Formats the value using the given formatter.

impl Clone for NormalGamma

fn clone(&self) -> NormalGamma

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Rv<Gaussian> for NormalGamma

fn ln_f(&self, x: &Gaussian) -> f64

Un-normalized probability function

fn ln_normalizer(&self) -> f64

The log of the constant term in the PDF/PMF. Should not be a function of any of the parameters.

fn draw<R: Rng>(&self, rng: &mut R) -> Gaussian

Single draw from the Rv

fn f(&self, x: &X) -> f64

Un-normalized probability function

fn normalizer(&self) -> f64

The constant term in the PDF/PMF. Should not be a function of any of the parameters.

fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<X>

Multiple draws of the Rv

impl Support<Gaussian> for NormalGamma

fn contains(&self, x: &Gaussian) -> bool

Returns true if x is in the support of the Rv

impl HasSuffStat<f64> for NormalGamma

type Stat = GaussianSuffStat

fn empty_suffstat(&self) -> Self::Stat

impl ContinuousDistr<Gaussian> for NormalGamma

fn pdf(&self, x: &X) -> f64

The value of the Probability Density Function (PDF) at x

fn ln_pdf(&self, x: &X) -> f64

The value of the log Probability Density Function (PDF) at x

impl ConjugatePrior<f64, Gaussian> for NormalGamma

type Posterior = Self

fn posterior(&self, x: &DataOrSuffStat<f64, Gaussian>) -> Self

Computes the posterior distribution from the data

fn ln_m(&self, x: &DataOrSuffStat<f64, Gaussian>) -> f64

Log marginal likelihood

fn ln_pp(&self, y: &f64, x: &DataOrSuffStat<f64, Gaussian>) -> f64

Log posterior predictive of y given x

fn m(&self, x: &DataOrSuffStat<X, Fx>) -> f64

Marginal likelihood of x

fn pp(&self, y: &X, x: &DataOrSuffStat<X, Fx>) -> f64

Posterior Predictive distribution