Struct Beta

pub struct Beta(/* private fields */);

Implementations

impl Beta

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

pub fn new_unchecked(alpha: f64, beta: f64) -> Beta

Trait Implementations

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 ContinuousDistribution for Beta

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

Evaluates the probability density function (PDF) at \(x\).

fn log_pdf(&self, x: &Sample<Self>) -> f64

Evaluates the log PDF at \(x\).

impl Debug for Beta

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

Formats the value using the given formatter.

impl Default for Beta

fn default() -> Beta

Returns the “default value” for a type.

impl Display for Beta

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

Formats the value using the given formatter.

impl Distribution for Beta

type Support = Interval

Support of sample elements.

type Params = Params

Parameter set uniquely defining the instance.

fn support(&self) -> Interval

Returns an instance of the support Space, Self::Support.

fn params(&self) -> Params

Returns an instance of the distribution parameters, Self::Params.

fn cdf(&self, x: &f64) -> Probability

Evaluates the cumulative distribution function (CDF) at \(x\).

fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> f64

Draw a random value from the distribution support.

fn into_support(self) -> Self::Support

Converts self into an instance of Self::Support.

fn into_params(self) -> Self::Params

Converts self into an instance of Self::Params.

fn ccdf(&self, x: &Sample<Self>) -> Probability

Evaluates the complementary CDF at \(x\).

fn log_cdf(&self, x: &Sample<Self>) -> f64

Evaluates the log CDF at \(x\), i.e. \(\ln{F(x)}\).

fn log_ccdf(&self, x: &Sample<Self>) -> f64

Evaluates the log complementary CDF at \(x\), i.e. \(\ln{(1 - F(x))}\).

fn sample_n<R: Rng + ?Sized>(&self, rng: &mut R, n: usize) -> Vec<Sample<Self>>

Draw n random value from the distribution support.

fn sample_iter<'a, R: Rng + ?Sized>( &'a self, rng: &'a mut R, ) -> Sampler<'a, Self, R>

Draw an indefinite number of random values from the distribution support.

impl From<Params> for Beta

fn from(from: Params) -> Beta

Converts to this type from the input type.

impl Likelihood for Beta

fn log_likelihood(&self, samples: &[f64]) -> f64

Computes the log-likelihood \(l(\theta \mid \bm{x})\).

fn likelihood(&self, samples: &[Sample<Self>]) -> f64

Computes the likelihood \(\mathcal{L}(\theta \mid \bm{x})\).

impl Modes for Beta

fn modes(&self) -> Vec<f64>

Computes the mode(s) of the distribution.

impl Quantiles for Beta

fn quantile(&self, _: Probability) -> f64

Evaluates the inverse cumulative distribution function (CDF) at p.

fn median(&self) -> f64

Computes the median value of the distribution, Q(0.5).

fn cquantile(&self, p: Probability) -> f64

Evaluates the complementary quantile function at x: Q(1 - p).

fn lower_quartile(&self) -> f64

Computes the lower quartile of the distribution, Q(0.25).

fn upper_quartile(&self) -> f64

Computes the upper quartile of the distribution, Q(0.75).

fn iqr(&self) -> f64

Computes the interquartile range (IQR) of the distribution, Q(0.75) - Q(0.25).

fn lower_fence(&self) -> f64

Computes the lower fence of the distribution, Q(0.25) - 1.5 · IQR.

fn upper_fence(&self) -> f64

Computes the lower fence of the distribution, Q(0.75) + 1.5 · IQR.

impl Score for Beta

type Grad = Grad

fn score(&self, samples: &[f64]) -> Grad

Computes the score function \(s(\theta) = \frac{\partial l(\theta \mid \bm{x})}{\partial \theta}\).

impl ShannonEntropy for Beta

fn shannon_entropy(&self) -> f64

Computes the Shannon entropy of the distribution, \(H(X)\).

impl UnivariateMoments for Beta

fn mean(&self) -> f64

Computes the expected value of the distribution.

fn variance(&self) -> f64

Computes the variance of the distribution.

fn skewness(&self) -> f64

Computes the skewness of the distribution.

fn excess_kurtosis(&self) -> f64

Computes the excess kurtosis of the distribution.

fn standard_deviation(&self) -> f64

Computes the standard deviation of the distribution.

fn kurtosis(&self) -> f64

Computes the kurtosis of the distribution.