Trait rstat::DiscreteDistribution

pub trait DiscreteDistribution: Distribution {
    fn pmf(&self, x: &Sample<Self>) -> Probability { ... }
    fn log_pmf(&self, x: &Sample<Self>) -> f64 { ... }
}

Trait for distributions over a countable Support.

The PMF is defined as the probability that a random variable \(X\) takes a value exactly equal to \(x\), i.e. \(f(x) = P(X = x) = P(s \in S : X(s) = x)\). We require that all sum of probabilities over all possible outcomes sums to 1.

Provided methods

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

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

Examples

let dist = Binomial::new_unchecked(5, Probability::new_unchecked(0.75));

assert_eq!(dist.pmf(&0), Probability::new_unchecked(0.0009765625));
assert_eq!(dist.pmf(&1), Probability::new_unchecked(0.0146484375));
assert_eq!(dist.pmf(&2), Probability::new_unchecked(0.087890625));
assert_eq!(dist.pmf(&3), Probability::new_unchecked(0.263671875));
assert_eq!(dist.pmf(&4), Probability::new_unchecked(0.3955078125));
assert_eq!(dist.pmf(&5), Probability::new_unchecked(0.2373046875));

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

Evaluates the log PMF at \(x\).

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Implementors

impl DiscreteDistribution for Multinomial

fn pmf(&self, xs: &Vec<usize>) -> Probability

fn log_pmf(&self, xs: &Vec<usize>) -> f64

impl DiscreteDistribution for Bernoulli

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

impl DiscreteDistribution for BetaBinomial

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

impl DiscreteDistribution for Binomial

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

impl DiscreteDistribution for Categorical

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

impl DiscreteDistribution for Geometric

fn pmf(&self, k: &u64) -> Probability

impl DiscreteDistribution for Poisson

fn log_pmf(&self, k: &u64) -> f64

impl DiscreteDistribution for Uniform<i64>

fn pmf(&self, x: &i64) -> Probability

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