Struct rv::dist::NormalGamma

pub struct NormalGamma { /* fields omitted */ }

Prior for Gaussian

Given x ~ N(μ, σ), the Normal Gamma prior implies that μ ~ N(m, 1/(rρ)) and ρ ~ Gamma(ν/2, s/2).

Methods

impl NormalGamma

pub fn set_m(&mut self, val: f64) -> &mut Self

pub fn set_r(&mut self, val: f64) -> &mut Self

pub fn set_s(&mut self, val: f64) -> &mut Self

pub fn set_v(&mut self, val: f64) -> &mut Self

impl NormalGamma

pub fn new(m: f64, r: f64, s: f64, v: f64) -> Result<Self, NormalGammaError>

Create a new Normal Gamma distribution

Arguments

• m: The prior mean
• r: Relative precision of μ versus data
• s: The mean of rho (the precision) is v/s.
• v: Degrees of freedom of precision of rho

pub fn new_unchecked(m: f64, r: f64, s: f64, v: f64) -> Self

Creates a new NormalGamma without checking whether the parameters are valid.

pub fn m(&self) -> f64

Get the m parameter

pub fn r(&self) -> f64

Get the r parameter

pub fn s(&self) -> f64

Get the s parameter

pub fn v(&self) -> f64

Get the v parameter

Trait Implementations

impl Clone for NormalGamma

fn clone(&self) -> NormalGamma

Returns a copy of the value.

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

Performs copy-assignment from source.

impl ConjugatePrior<f64, Gaussian> for NormalGamma

type Posterior = Self

fn posterior(&self, x: &DataOrSuffStat<f64, Gaussian>) -> Self

Computes the posterior distribution from the data

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

Log marginal likelihood

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

impl ContinuousDistr<Gaussian> for NormalGamma

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 Debug for NormalGamma

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

Formats the value using the given formatter.

impl Display for NormalGamma

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

Formats the value using the given formatter.

impl<'_> From<&'_ NormalGamma> for String

fn from(ng: &NormalGamma) -> String

Performs the conversion.

impl HasSuffStat<f64> for NormalGamma

type Stat = GaussianSuffStat

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

impl PartialEq<NormalGamma> for NormalGamma

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

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

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

This method tests for !=.

impl PartialOrd<NormalGamma> for NormalGamma

fn partial_cmp(&self, other: &NormalGamma) -> Option<Ordering>

This method returns an ordering between self and other values if one exists.

fn lt(&self, other: &NormalGamma) -> bool

This method tests less than (for self and other) and is used by the < operator.

fn le(&self, other: &NormalGamma) -> bool

This method tests less than or equal to (for self and other) and is used by the <= operator.

fn gt(&self, other: &NormalGamma) -> bool

This method tests greater than (for self and other) and is used by the > operator.

fn ge(&self, other: &NormalGamma) -> bool

This method tests greater than or equal to (for self and other) and is used by the >= operator.

impl Rv<Gaussian> for NormalGamma

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

Probability function

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

Single draw from the Rv

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

Probability function

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

Multiple draws of the Rv

impl StructuralPartialEq for NormalGamma

impl Support<Gaussian> for NormalGamma

fn supports(&self, x: &Gaussian) -> bool

Returns true if x is in the support of the Rv