Struct rv::dist::Bernoulli

pub struct Bernoulli {
    pub p: f64,
}

Bernoulli distribution with success probability p

Examples

use rv::prelude::*;

let b = Bernoulli::new(0.75).unwrap();
assert!((b.pmf(&true) - 0.75).abs() < 1E-12);

Fields

p: f64

Probability of a success (x=1)

Methods

impl Bernoulli

pub fn new(p: f64) -> Result<Self>

pub fn uniform() -> Self

A Bernoulli distribution with a 50% chance of success

pub fn q(&self) -> f64

The complement of p, i.e. (1 - p).

Trait Implementations

impl Debug for Bernoulli

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

Formats the value using the given formatter.

impl Clone for Bernoulli

fn clone(&self) -> Bernoulli

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Default for Bernoulli

fn default() -> Self

Returns the "default value" for a type.

impl KlDivergence for Bernoulli

fn kl(&self, other: &Self) -> f64

The KL divergence, KL(P|Q) between this distribution, P, and another, Q

fn kl_sym(&self, other: &Self) -> f64

Symmetrised divergence, KL(P|Q) + KL(Q|P)

impl Entropy for Bernoulli

fn entropy(&self) -> f64

The entropy, H(X)

impl Skewness for Bernoulli

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

impl Kurtosis for Bernoulli

fn kurtosis(&self) -> Option<f64>

impl Mean<f64> for Bernoulli

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

Returns None if the mean is undefined

impl Median<f64> for Bernoulli

fn median(&self) -> Option<f64>

Returns None if the median is undefined

impl Variance<f64> for Bernoulli

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

Returns None if the variance is undefined

impl Rv<bool> for Bernoulli

fn f(&self, x: &bool) -> f64

Un-normalized probability function

fn ln_f(&self, x: &bool) -> f64

Un-normalized probability function

fn ln_normalizer(&self) -> f64

The log of the constant term in the PDF/PMF. Should not be a function of any of the parameters.

fn draw<R: Rng>(&self, rng: &mut R) -> bool

Single draw from the Rv

fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<bool>

Multiple draws of the Rv

fn normalizer(&self) -> f64

The constant term in the PDF/PMF. Should not be a function of any of the parameters.

impl Support<bool> for Bernoulli

fn contains(&self, _x: &bool) -> bool

Returns true if x is in the support of the Rv

impl DiscreteDistr<bool> for Bernoulli

fn pmf(&self, x: &bool) -> f64

Probability mass function (PMF) at x

fn ln_pmf(&self, x: &bool) -> f64

Natural logarithm of the probability mass function (PMF)

impl Cdf<bool> for Bernoulli

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

The value of the Cumulative Density Function at x

fn sf(&self, x: &X) -> f64

Survival function, 1 - CDF(x)

impl Mode<bool> for Bernoulli

fn mode(&self) -> Option<bool>

Returns None if the mode is undefined or is not a single value

impl HasSuffStat<bool> for Bernoulli

type Stat = BernoulliSuffStat

fn empty_suffstat(&self) -> Self::Stat

impl Rv<u8> for Bernoulli

fn f(&self, x: &u8) -> f64

Un-normalized probability function

fn ln_f(&self, x: &u8) -> f64

Un-normalized probability function

fn ln_normalizer(&self) -> f64

The log of the constant term in the PDF/PMF. Should not be a function of any of the parameters.

fn draw<R: Rng>(&self, rng: &mut R) -> u8

Single draw from the Rv

fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<u8>

Multiple draws of the Rv

fn normalizer(&self) -> f64

The constant term in the PDF/PMF. Should not be a function of any of the parameters.

impl Support<u8> for Bernoulli

fn contains(&self, x: &u8) -> bool

Returns true if x is in the support of the Rv

impl DiscreteDistr<u8> for Bernoulli

fn pmf(&self, x: &u8) -> f64

Probability mass function (PMF) at x

fn ln_pmf(&self, x: &u8) -> f64

Natural logarithm of the probability mass function (PMF)

impl Cdf<u8> for Bernoulli

fn cdf(&self, x: &u8) -> f64

The value of the Cumulative Density Function at x

fn sf(&self, x: &X) -> f64

Survival function, 1 - CDF(x)

impl Mode<u8> for Bernoulli

fn mode(&self) -> Option<u8>

Returns None if the mode is undefined or is not a single value

impl HasSuffStat<u8> for Bernoulli

type Stat = BernoulliSuffStat

fn empty_suffstat(&self) -> Self::Stat

impl Rv<u16> for Bernoulli

fn f(&self, x: &u16) -> f64

Un-normalized probability function

fn ln_f(&self, x: &u16) -> f64

Un-normalized probability function

fn ln_normalizer(&self) -> f64

The log of the constant term in the PDF/PMF. Should not be a function of any of the parameters.

fn draw<R: Rng>(&self, rng: &mut R) -> u16

Single draw from the Rv

fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<u16>

Multiple draws of the Rv

fn normalizer(&self) -> f64

The constant term in the PDF/PMF. Should not be a function of any of the parameters.

impl Support<u16> for Bernoulli

fn contains(&self, x: &u16) -> bool

Returns true if x is in the support of the Rv

impl DiscreteDistr<u16> for Bernoulli

fn pmf(&self, x: &u16) -> f64

Probability mass function (PMF) at x

fn ln_pmf(&self, x: &u16) -> f64

Natural logarithm of the probability mass function (PMF)

impl Cdf<u16> for Bernoulli

fn cdf(&self, x: &u16) -> f64

The value of the Cumulative Density Function at x

fn sf(&self, x: &X) -> f64

Survival function, 1 - CDF(x)

impl Mode<u16> for Bernoulli

fn mode(&self) -> Option<u16>

Returns None if the mode is undefined or is not a single value

impl HasSuffStat<u16> for Bernoulli

type Stat = BernoulliSuffStat

fn empty_suffstat(&self) -> Self::Stat

impl Rv<u32> for Bernoulli

fn f(&self, x: &u32) -> f64

Un-normalized probability function

fn ln_f(&self, x: &u32) -> f64

Un-normalized probability function

fn ln_normalizer(&self) -> f64

The log of the constant term in the PDF/PMF. Should not be a function of any of the parameters.

fn draw<R: Rng>(&self, rng: &mut R) -> u32

Single draw from the Rv

fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<u32>

Multiple draws of the Rv

fn normalizer(&self) -> f64

The constant term in the PDF/PMF. Should not be a function of any of the parameters.

impl Support<u32> for Bernoulli

fn contains(&self, x: &u32) -> bool

Returns true if x is in the support of the Rv

impl DiscreteDistr<u32> for Bernoulli

fn pmf(&self, x: &u32) -> f64

Probability mass function (PMF) at x

fn ln_pmf(&self, x: &u32) -> f64

Natural logarithm of the probability mass function (PMF)

impl Cdf<u32> for Bernoulli

fn cdf(&self, x: &u32) -> f64

The value of the Cumulative Density Function at x

fn sf(&self, x: &X) -> f64

Survival function, 1 - CDF(x)

impl Mode<u32> for Bernoulli

fn mode(&self) -> Option<u32>

Returns None if the mode is undefined or is not a single value

impl HasSuffStat<u32> for Bernoulli

type Stat = BernoulliSuffStat

fn empty_suffstat(&self) -> Self::Stat

impl Rv<u64> for Bernoulli

fn f(&self, x: &u64) -> f64

Un-normalized probability function

fn ln_f(&self, x: &u64) -> f64

Un-normalized probability function

fn ln_normalizer(&self) -> f64

The log of the constant term in the PDF/PMF. Should not be a function of any of the parameters.

fn draw<R: Rng>(&self, rng: &mut R) -> u64

Single draw from the Rv

fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<u64>

Multiple draws of the Rv

fn normalizer(&self) -> f64

The constant term in the PDF/PMF. Should not be a function of any of the parameters.

impl Support<u64> for Bernoulli

fn contains(&self, x: &u64) -> bool

Returns true if x is in the support of the Rv

impl DiscreteDistr<u64> for Bernoulli

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

Probability mass function (PMF) at x

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

Natural logarithm of the probability mass function (PMF)

impl Cdf<u64> for Bernoulli

fn cdf(&self, x: &u64) -> f64

The value of the Cumulative Density Function at x

fn sf(&self, x: &X) -> f64

Survival function, 1 - CDF(x)

impl Mode<u64> for Bernoulli

fn mode(&self) -> Option<u64>

Returns None if the mode is undefined or is not a single value

impl HasSuffStat<u64> for Bernoulli

type Stat = BernoulliSuffStat

fn empty_suffstat(&self) -> Self::Stat

impl Rv<usize> for Bernoulli

fn f(&self, x: &usize) -> f64

Un-normalized probability function

fn ln_f(&self, x: &usize) -> f64

Un-normalized probability function

fn ln_normalizer(&self) -> f64

The log of the constant term in the PDF/PMF. Should not be a function of any of the parameters.

fn draw<R: Rng>(&self, rng: &mut R) -> usize

Single draw from the Rv

fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<usize>

Multiple draws of the Rv

fn normalizer(&self) -> f64

The constant term in the PDF/PMF. Should not be a function of any of the parameters.

impl Support<usize> for Bernoulli

fn contains(&self, x: &usize) -> bool

Returns true if x is in the support of the Rv

impl DiscreteDistr<usize> for Bernoulli

fn pmf(&self, x: &usize) -> f64

Probability mass function (PMF) at x

fn ln_pmf(&self, x: &usize) -> f64

Natural logarithm of the probability mass function (PMF)

impl Cdf<usize> for Bernoulli

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

The value of the Cumulative Density Function at x

fn sf(&self, x: &X) -> f64

Survival function, 1 - CDF(x)

impl Mode<usize> for Bernoulli

fn mode(&self) -> Option<usize>

Returns None if the mode is undefined or is not a single value

impl HasSuffStat<usize> for Bernoulli

type Stat = BernoulliSuffStat

fn empty_suffstat(&self) -> Self::Stat

impl Rv<i8> for Bernoulli

fn f(&self, x: &i8) -> f64

Un-normalized probability function

fn ln_f(&self, x: &i8) -> f64

Un-normalized probability function

fn ln_normalizer(&self) -> f64

The log of the constant term in the PDF/PMF. Should not be a function of any of the parameters.

fn draw<R: Rng>(&self, rng: &mut R) -> i8

Single draw from the Rv

fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<i8>

Multiple draws of the Rv

fn normalizer(&self) -> f64

The constant term in the PDF/PMF. Should not be a function of any of the parameters.

impl Support<i8> for Bernoulli

fn contains(&self, x: &i8) -> bool

Returns true if x is in the support of the Rv

impl DiscreteDistr<i8> for Bernoulli

fn pmf(&self, x: &i8) -> f64

Probability mass function (PMF) at x

fn ln_pmf(&self, x: &i8) -> f64

Natural logarithm of the probability mass function (PMF)

impl Cdf<i8> for Bernoulli

fn cdf(&self, x: &i8) -> f64

The value of the Cumulative Density Function at x

fn sf(&self, x: &X) -> f64

Survival function, 1 - CDF(x)

impl Mode<i8> for Bernoulli

fn mode(&self) -> Option<i8>

Returns None if the mode is undefined or is not a single value

impl HasSuffStat<i8> for Bernoulli

type Stat = BernoulliSuffStat

fn empty_suffstat(&self) -> Self::Stat

impl Rv<i16> for Bernoulli

fn f(&self, x: &i16) -> f64

Un-normalized probability function

fn ln_f(&self, x: &i16) -> f64

Un-normalized probability function

fn ln_normalizer(&self) -> f64

The log of the constant term in the PDF/PMF. Should not be a function of any of the parameters.

fn draw<R: Rng>(&self, rng: &mut R) -> i16

Single draw from the Rv

fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<i16>

Multiple draws of the Rv

fn normalizer(&self) -> f64

The constant term in the PDF/PMF. Should not be a function of any of the parameters.

impl Support<i16> for Bernoulli

fn contains(&self, x: &i16) -> bool

Returns true if x is in the support of the Rv

impl DiscreteDistr<i16> for Bernoulli

fn pmf(&self, x: &i16) -> f64

Probability mass function (PMF) at x

fn ln_pmf(&self, x: &i16) -> f64

Natural logarithm of the probability mass function (PMF)

impl Cdf<i16> for Bernoulli

fn cdf(&self, x: &i16) -> f64

The value of the Cumulative Density Function at x

fn sf(&self, x: &X) -> f64

Survival function, 1 - CDF(x)

impl Mode<i16> for Bernoulli

fn mode(&self) -> Option<i16>

Returns None if the mode is undefined or is not a single value

impl HasSuffStat<i16> for Bernoulli

type Stat = BernoulliSuffStat

fn empty_suffstat(&self) -> Self::Stat

impl Rv<i32> for Bernoulli

fn f(&self, x: &i32) -> f64

Un-normalized probability function

fn ln_f(&self, x: &i32) -> f64

Un-normalized probability function

fn ln_normalizer(&self) -> f64

The log of the constant term in the PDF/PMF. Should not be a function of any of the parameters.

fn draw<R: Rng>(&self, rng: &mut R) -> i32

Single draw from the Rv

fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<i32>

Multiple draws of the Rv

fn normalizer(&self) -> f64

The constant term in the PDF/PMF. Should not be a function of any of the parameters.

impl Support<i32> for Bernoulli

fn contains(&self, x: &i32) -> bool

Returns true if x is in the support of the Rv

impl DiscreteDistr<i32> for Bernoulli

fn pmf(&self, x: &i32) -> f64

Probability mass function (PMF) at x

fn ln_pmf(&self, x: &i32) -> f64

Natural logarithm of the probability mass function (PMF)

impl Cdf<i32> for Bernoulli

fn cdf(&self, x: &i32) -> f64

The value of the Cumulative Density Function at x

fn sf(&self, x: &X) -> f64

Survival function, 1 - CDF(x)

impl Mode<i32> for Bernoulli

fn mode(&self) -> Option<i32>

Returns None if the mode is undefined or is not a single value

impl HasSuffStat<i32> for Bernoulli

type Stat = BernoulliSuffStat

fn empty_suffstat(&self) -> Self::Stat

impl Rv<i64> for Bernoulli

fn f(&self, x: &i64) -> f64

Un-normalized probability function

fn ln_f(&self, x: &i64) -> f64

Un-normalized probability function

fn ln_normalizer(&self) -> f64

The log of the constant term in the PDF/PMF. Should not be a function of any of the parameters.

fn draw<R: Rng>(&self, rng: &mut R) -> i64

Single draw from the Rv

fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<i64>

Multiple draws of the Rv

fn normalizer(&self) -> f64

The constant term in the PDF/PMF. Should not be a function of any of the parameters.

impl Support<i64> for Bernoulli

fn contains(&self, x: &i64) -> bool

Returns true if x is in the support of the Rv

impl DiscreteDistr<i64> for Bernoulli

fn pmf(&self, x: &i64) -> f64

Probability mass function (PMF) at x

fn ln_pmf(&self, x: &i64) -> f64

Natural logarithm of the probability mass function (PMF)

impl Cdf<i64> for Bernoulli

fn cdf(&self, x: &i64) -> f64

The value of the Cumulative Density Function at x

fn sf(&self, x: &X) -> f64

Survival function, 1 - CDF(x)

impl Mode<i64> for Bernoulli

fn mode(&self) -> Option<i64>

Returns None if the mode is undefined or is not a single value

impl HasSuffStat<i64> for Bernoulli

type Stat = BernoulliSuffStat

fn empty_suffstat(&self) -> Self::Stat

impl Rv<isize> for Bernoulli

fn f(&self, x: &isize) -> f64

Un-normalized probability function

fn ln_f(&self, x: &isize) -> f64

Un-normalized probability function

fn ln_normalizer(&self) -> f64

The log of the constant term in the PDF/PMF. Should not be a function of any of the parameters.

fn draw<R: Rng>(&self, rng: &mut R) -> isize

Single draw from the Rv

fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<isize>

Multiple draws of the Rv

fn normalizer(&self) -> f64

The constant term in the PDF/PMF. Should not be a function of any of the parameters.

impl Support<isize> for Bernoulli

fn contains(&self, x: &isize) -> bool

Returns true if x is in the support of the Rv

impl DiscreteDistr<isize> for Bernoulli

fn pmf(&self, x: &isize) -> f64

Probability mass function (PMF) at x

fn ln_pmf(&self, x: &isize) -> f64

Natural logarithm of the probability mass function (PMF)

impl Cdf<isize> for Bernoulli

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

The value of the Cumulative Density Function at x

fn sf(&self, x: &X) -> f64

Survival function, 1 - CDF(x)

impl Mode<isize> for Bernoulli

fn mode(&self) -> Option<isize>

Returns None if the mode is undefined or is not a single value

impl HasSuffStat<isize> for Bernoulli

type Stat = BernoulliSuffStat

fn empty_suffstat(&self) -> Self::Stat

impl Rv<Bernoulli> for Beta

fn ln_f(&self, x: &Bernoulli) -> f64

Un-normalized probability function

fn ln_normalizer(&self) -> f64

The log of the constant term in the PDF/PMF. Should not be a function of any of the parameters.

fn draw<R: Rng>(&self, rng: &mut R) -> Bernoulli

Single draw from the Rv

fn f(&self, x: &X) -> f64

Un-normalized probability function

fn normalizer(&self) -> f64

The constant term in the PDF/PMF. Should not be a function of any of the parameters.

fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<X>

Multiple draws of the Rv

impl Support<Bernoulli> for Beta

fn contains(&self, x: &Bernoulli) -> bool

Returns true if x is in the support of the Rv

impl ContinuousDistr<Bernoulli> for Beta

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

The value of the Probability Density Function (PDF) at x

fn ln_pdf(&self, x: &X) -> f64

The value of the log Probability Density Function (PDF) at x

impl ConjugatePrior<bool, Bernoulli> for Beta

type Posterior = Self

fn posterior(&self, x: &DataOrSuffStat<bool, Bernoulli>) -> Self

Computes the posterior distribution from the data

fn ln_m(&self, x: &DataOrSuffStat<bool, Bernoulli>) -> f64

Log marginal likelihood

fn ln_pp(&self, y: &bool, x: &DataOrSuffStat<bool, Bernoulli>) -> f64

Log posterior predictive of y given x

fn m(&self, x: &DataOrSuffStat<X, Fx>) -> f64

Marginal likelihood of x

fn pp(&self, y: &X, x: &DataOrSuffStat<X, Fx>) -> f64

Posterior Predictive distribution