Struct rv::dist::Dirichlet

pub struct Dirichlet {
    pub alphas: Vec<f64>,
}

Dirichlet distribution over points on the k-simplex

Fields

alphas: Vec<f64>

A Vec of real numbers in (0, ∞)

Methods

impl Dirichlet

pub fn new(alphas: Vec<f64>) -> Result<Self>

Creates a Dirichlet with a given alphas vector

pub fn symmetric(alpha: f64, k: usize) -> Self

Creates a Dirichlet where all alphas are identical

Eaxmples

let dir = Dirichlet::symmetric(1.0, 4);
assert_eq!(dir.alphas, vec![1.0, 1.0, 1.0, 1.0]);

pub fn jeffreys(k: usize) -> Self

Creates a Dirichlet with all alphas = 0.5 (Feffreys prior)

Eaxmples

let dir = Dirichlet::jeffreys(3);
assert_eq!(dir.alphas, vec![0.5, 0.5, 0.5]);

pub fn k(&self) -> usize

The length of alphas / the number of categories

Trait Implementations

impl Debug for Dirichlet

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

Formats the value using the given formatter.

impl Clone for Dirichlet

fn clone(&self) -> Dirichlet

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Rv<Vec<f64>> for Dirichlet

fn draw<R: Rng>(&self, rng: &mut R) -> Vec<f64>

Single draw from the Rv

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 ln_f(&self, x: &Vec<f64>) -> f64

Un-normalized probability function

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 ContinuousDistr<Vec<f64>> for Dirichlet

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 Support<Vec<f64>> for Dirichlet

fn contains(&self, x: &Vec<f64>) -> bool

Returns true if x is in the support of the Rv

impl Rv<Categorical> for Dirichlet

fn ln_f(&self, x: &Categorical) -> 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) -> Categorical

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<X: CategoricalDatum> ConjugatePrior<X, Categorical> for Dirichlet

type Posterior = Self

fn posterior(&self, x: &CategoricalData<X>) -> Self::Posterior

Computes the posterior distribution from the data

fn ln_m(&self, x: &CategoricalData<X>) -> f64

Log marginal likelihood

fn ln_pp(&self, y: &X, x: &CategoricalData<X>) -> 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