Struct Binomial

pub struct Binomial { /* private fields */ }

Implementations

impl Binomial

pub fn new(n: usize, p: Probability) -> Result<Binomial, Error>

pub fn new_unchecked(n: usize, p: Probability) -> Binomial

Trait Implementations

impl Clone for Binomial

fn clone(&self) -> Binomial

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Convolution<Bernoulli> for Binomial

type Output = Binomial

The resulting Distribution type.

fn convolve(self, rv: Bernoulli) -> Result<Binomial, Error>

Return the unweighted linear sum of self with another Distribution of type T.

fn convolve_many(self, rvs: Vec<T>) -> Result<Self::Output, Error>

where Self::Output: Convolution<T, Output = Self::Output>, Self: Sized,

Return the unweighted linear sum of self with a set of Distributions of type T.

impl Convolution for Binomial

type Output = Binomial

The resulting Distribution type.

fn convolve(self, rv: Binomial) -> Result<Binomial, Error>

Return the unweighted linear sum of self with another Distribution of type T.

fn convolve_many(self, rvs: Vec<T>) -> Result<Self::Output, Error>

where Self::Output: Convolution<T, Output = Self::Output>, Self: Sized,

Return the unweighted linear sum of self with a set of Distributions of type T.

impl Debug for Binomial

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

Formats the value using the given formatter.

impl DiscreteDistribution for Binomial

fn pmf(&self, k: &usize) -> Probability

Evaluates the probability mass function (PMF) at \(x\).

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

Evaluates the log PMF at \(x\).

impl Display for Binomial

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

Formats the value using the given formatter.

impl Distribution for Binomial

type Support = Ordinal

Support of sample elements.

type Params = Params

Parameter set uniquely defining the instance.

fn support(&self) -> Ordinal

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, k: &usize) -> Probability

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

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

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 FisherInformation for Binomial

fn fisher_information(&self) -> Array2<f64>

Computes the Fisher information matrix of the distribution, \(I(X)\).

impl From<Params> for Binomial

fn from(params: Params) -> Binomial

Converts to this type from the input type.

impl Modes for Binomial

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

Computes the mode(s) of the distribution.

impl Quantiles for Binomial

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 ShannonEntropy for Binomial

fn shannon_entropy(&self) -> f64

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

impl UnivariateMoments for Binomial

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 kurtosis(&self) -> f64

Computes the kurtosis of the distribution.

fn standard_deviation(&self) -> f64

Computes the standard deviation of the distribution.

fn excess_kurtosis(&self) -> f64

Computes the excess kurtosis of the distribution.

impl Copy for Binomial