Struct statrs::distribution::Multinomial

pub struct Multinomial { /* private fields */ }

Implements the Multinomial distribution which is a generalization of the Binomial distribution

Examples

use statrs::distribution::Multinomial;
use statrs::statistics::MeanN;
use nalgebra::DVector;

let n = Multinomial::new(&[0.3, 0.7], 5).unwrap();
assert_eq!(n.mean().unwrap(), DVector::from_vec(vec![1.5, 3.5]));

Implementations

impl Multinomial

pub fn new(p: &[f64], n: u64) -> Result<Multinomial>

Constructs a new multinomial distribution with probabilities p and n number of trials.

Errors

Returns an error if p is empty, the sum of the elements in p is 0, or any element in p is less than 0 or is f64::NAN

Note

The elements in p do not need to be normalized

Examples

use statrs::distribution::Multinomial;

let mut result = Multinomial::new(&[0.0, 1.0, 2.0], 3);
assert!(result.is_ok());

result = Multinomial::new(&[0.0, -1.0, 2.0], 3);
assert!(result.is_err());

pub fn p(&self) -> &[f64]

Returns the probabilities of the multinomial distribution as a slice

Examples

use statrs::distribution::Multinomial;

let n = Multinomial::new(&[0.0, 1.0, 2.0], 3).unwrap();
assert_eq!(n.p(), [0.0, 1.0, 2.0]);

pub fn n(&self) -> u64

Returns the number of trials of the multinomial distribution

Examples

use statrs::distribution::Multinomial;

let n = Multinomial::new(&[0.0, 1.0, 2.0], 3).unwrap();
assert_eq!(n.n(), 3);

Trait Implementations

impl Clone for Multinomial

fn clone(&self) -> Multinomial

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for Multinomial

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

Formats the value using the given formatter.

impl<'a> Discrete<&'a [u64], f64> for Multinomial

fn pmf(&self, x: &[u64]) -> f64

Calculates the probability mass function for the multinomial distribution with the given x’s corresponding to the probabilities for this distribution

Panics

If the elements in x do not sum to n or if the length of x is not equivalent to the length of p

Formula

(n! / x_1!...x_k!) * p_i^x_i for i in 1...k

where n is the number of trials, p_i is the ith probability, x_i is the ith x value, and k is the total number of probabilities

fn ln_pmf(&self, x: &[u64]) -> f64

Calculates the log probability mass function for the multinomial distribution with the given x’s corresponding to the probabilities for this distribution

Panics

If the elements in x do not sum to n or if the length of x is not equivalent to the length of p

Formula

ln((n! / x_1!...x_k!) * p_i^x_i) for i in 1...k

where n is the number of trials, p_i is the ith probability, x_i is the ith x value, and k is the total number of probabilities

impl Distribution<Vec<f64>> for Multinomial

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

Generate a random value of T, using rng as the source of randomness.

fn sample_iter<R>(self, rng: R) -> DistIter<Self, R, T>

where R: Rng, Self: Sized,

Create an iterator that generates random values of T, using rng as the source of randomness.

fn map<F, S>(self, func: F) -> DistMap<Self, F, T, S>

where F: Fn(T) -> S, Self: Sized,

Create a distribution of values of ‘S’ by mapping the output of Self through the closure F

impl MeanN<Matrix<f64, Dyn, Const<1>, VecStorage<f64, Dyn, Const<1>>>> for Multinomial

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

Returns the mean of the multinomial distribution

Formula

n * p_i for i in 1...k

where n is the number of trials, p_i is the ith probability, and k is the total number of probabilities

impl PartialEq for Multinomial

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

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

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

This method tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl VarianceN<Matrix<f64, Dyn, Dyn, VecStorage<f64, Dyn, Dyn>>> for Multinomial

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

Returns the variance of the multinomial distribution

Formula

n * p_i * (1 - p_i) for i in 1...k

where n is the number of trials, p_i is the ith probability, and k is the total number of probabilities

impl StructuralPartialEq for Multinomial