Struct statrs::distribution::Multinomial

pub struct Multinomial { /* fields omitted */ }

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

Examples

use statrs::distribution::Multinomial;
use statrs::statistics::Mean;

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

Methods

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

fn checked_pmf(&self, x: &[u64]) -> Result<f64>

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

Errors

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 checked_ln_pmf(&self, x: &[u64]) -> Result<f64>

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

Errors

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 Mean<Vec<f64>> for Multinomial

fn mean(&self) -> Vec<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 Variance<Vec<f64>> for Multinomial

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

Returns the variance of the multinomial distribution

Formula

n * p_i * (1 - p_1) 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

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

Returns the standard deviation of the multinomial distribution

Formula

sqrt(n * p_i * (1 - p_1)) 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 Skewness<Vec<f64>> for Multinomial

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

Returns the skewness of the multinomial distribution

Formula

(1 - 2 * p_i) / (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 PartialEq<Multinomial> 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: &Multinomial) -> bool

This method tests for !=.

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 Distribution<Vec<f64>> for Multinomial

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

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

fn sample_iter<R>(&'a self, rng: &'a mut R) -> DistIter<'a, Self, R, T> where
    R: Rng, 

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