Struct rv::dist::Beta

pub struct Beta {
    pub alpha: f64,
    pub beta: f64,
}

Beta distribution, Beta(α, β).

Fields

alpha: f64

beta: f64

Methods

impl Beta

pub fn new(alpha: f64, beta: f64) -> Result<Self>

pub fn uniform() -> Self

Create a Beta distribution with even density over (0, 1).

pub fn jeffreys() -> Self

Create a Beta distribution with the Jeffrey's parameterization, Beta(0.5, 0.5).

Trait Implementations

impl Debug for Beta

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

Formats the value using the given formatter.

impl Clone for Beta

fn clone(&self) -> Beta

Returns a copy of the value.

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

Performs copy-assignment from source.

impl PartialEq for Beta

fn eq(&self, other: &Beta) -> bool

This method tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Beta) -> bool

This method tests for !=.

impl Default for Beta

fn default() -> Self

Returns the "default value" for a type.

impl Variance<f64> for Beta

fn variance(&self) -> Option<f64>

Returns None if the variance is undefined

impl Entropy for Beta

fn entropy(&self) -> f64

The entropy, H(X)

impl Skewness for Beta

fn skewness(&self) -> Option<f64>

impl Kurtosis for Beta

fn kurtosis(&self) -> Option<f64>

impl Rv<f32> for Beta

fn ln_f(&self, x: &f32) -> f64

Un-normalized probability function

fn draw<R: Rng>(&self, rng: &mut R) -> f32

Single draw from the Rv

fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<f32>

Multiple draws of 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 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.

impl Support<f32> for Beta

fn contains(&self, x: &f32) -> bool

Returns true if x is in the support of the Rv

impl ContinuousDistr<f32> for Beta

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 Mean<f32> for Beta

fn mean(&self) -> Option<f32>

Returns None if the mean is undefined

impl Mode<f32> for Beta

fn mode(&self) -> Option<f32>

Returns None if the mode is undefined or is not a single value

impl Rv<f64> for Beta

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

Un-normalized probability function

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

Single draw from the Rv

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

Multiple draws of 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 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.

impl Support<f64> for Beta

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

Returns true if x is in the support of the Rv

impl ContinuousDistr<f64> for Beta

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 Mean<f64> for Beta

fn mean(&self) -> Option<f64>

Returns None if the mean is undefined

impl Mode<f64> for Beta

fn mode(&self) -> Option<f64>

Returns None if the mode is undefined or is not a single value

impl Rv<Bernoulli> for Beta

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

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<Bernoulli> for Beta

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

Returns true if x is in the support of the Rv

impl ContinuousDistr<Bernoulli> for Beta

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<bool, Bernoulli> for Beta

type Posterior = Self

fn posterior(&self, x: &DataOrSuffStat<bool, Bernoulli>) -> Self

Computes the posterior distribution from the data

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

Log marginal likelihood

fn ln_pp(&self, y: &bool, x: &DataOrSuffStat<bool, Bernoulli>) -> 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