Struct rv::dist::NormalInvWishart

pub struct NormalInvWishart { /* private fields */ }

Common conjugate prior on the μ and Σ parameters in the Multivariate Gaussian, Ν(μ, Σ)

Ν(μ, Σ) ~ NIW(μ₀, κ₀, ν, Ψ) implies μ ~ N(μ₀, Σ/k₀) and Σ ~ W^-1(Ψ, ν)

Example

Draw a Multivariate Gaussian from GIW

use nalgebra::{DMatrix, DVector};
use rv::prelude::*;

let mu = DVector::zeros(3);
let k = 1.0;
let df = 3;
let scale = DMatrix::identity(3, 3);

let niw = NormalInvWishart::new(mu, k, df, scale).unwrap();

let mut rng = rand::thread_rng();

let mvg: MvGaussian = niw.draw(&mut rng);

Implementations

impl NormalInvWishart

pub fn new( mu: DVector<f64>, k: f64, df: usize, scale: DMatrix<f64> ) -> Result<Self, NormalInvWishartError>

Create a new NormalInvWishart distribution

Arguments

• mu: The mean of μ, μ₀
• k: A scale factor on Σ, κ₀
• df: The degrees of freedom, ν > |μ| - 1
• scale The positive-definite scale matrix, Ψ

pub fn new_unchecked( mu: DVector<f64>, k: f64, df: usize, scale: DMatrix<f64> ) -> Self

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

pub fn ndims(&self) -> usize

Get the number of dimensions

pub fn mu(&self) -> &DVector<f64>

Get a reference to the mu vector

pub fn k(&self) -> f64

Get the k parameter

pub fn set_k(&mut self, k: f64) -> Result<(), NormalInvWishartError>

Set the value of k

pub fn set_k_unchecked(&mut self, k: f64)

Set the value of k without input validation

pub fn df(&self) -> usize

Get the degrees of freedom, df

pub fn set_df(&mut self, df: usize) -> Result<(), NormalInvWishartError>

Set the value of df

pub fn set_df_unchecked(&mut self, df: usize)

Set the value of df without input validation

pub fn scale(&self) -> &DMatrix<f64>

Get a reference to the scale matrix

pub fn set_scale( &mut self, scale: DMatrix<f64> ) -> Result<(), NormalInvWishartError>

Set the scale parameter

pub fn set_scale_unnchecked(&mut self, scale: DMatrix<f64>)

pub fn set_mu(&mut self, mu: DVector<f64>) -> Result<(), NormalInvWishartError>

Set the scale parameter

pub fn set_mu_unchecked(&mut self, mu: DVector<f64>)

Trait Implementations

impl Clone for NormalInvWishart

fn clone(&self) -> NormalInvWishart

Returns a copy of the value.

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

Performs copy-assignment from source.

impl ConjugatePrior<Matrix<f64, Dyn, Const<1>, VecStorage<f64, Dyn, Const<1>>>, MvGaussian> for NormalInvWishart

type Posterior = NormalInvWishart

Type of the posterior distribution

type LnMCache = f64

Type of the ln_m cache

type LnPpCache = (NormalInvWishart, f64)

Type of the ln_pp cache

fn posterior( &self, x: &DataOrSuffStat<'_, DVector<f64>, MvGaussian> ) -> NormalInvWishart

Computes the posterior distribution from the data

fn ln_m_cache(&self) -> f64

Compute the cache for the log marginal likelihood.

fn ln_m_with_cache( &self, cache: &Self::LnMCache, x: &DataOrSuffStat<'_, DVector<f64>, MvGaussian> ) -> f64

Log marginal likelihood with supplied cache.

fn ln_pp_cache( &self, x: &DataOrSuffStat<'_, DVector<f64>, MvGaussian> ) -> Self::LnPpCache

Compute the cache for the Log posterior predictive of y given x.

fn ln_pp_with_cache(&self, cache: &Self::LnPpCache, y: &DVector<f64>) -> f64

Log posterior predictive of y given x with supplied ln(norm)

fn ln_m(&self, x: &DataOrSuffStat<'_, X, Fx>) -> f64

The log marginal likelihood

fn ln_pp(&self, y: &X, x: &DataOrSuffStat<'_, X, Fx>) -> 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<MvGaussian> for NormalInvWishart

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 NormalInvWishart

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

Formats the value using the given formatter.

impl<'de> Deserialize<'de> for NormalInvWishart

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl Display for NormalInvWishart

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

Formats the value using the given formatter.

impl From<&NormalInvWishart> for String

fn from(niw: &NormalInvWishart) -> String

Converts to this type from the input type.

impl PartialEq<NormalInvWishart> for NormalInvWishart

fn eq(&self, other: &NormalInvWishart) -> 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 Rv<MvGaussian> for NormalInvWishart

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

Probability function

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

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

fn sample_stream<'r, R: Rng>( &'r self, rng: &'r mut R ) -> Box<dyn Iterator<Item = X> + 'r>

Create a never-ending iterator of samples

impl Serialize for NormalInvWishart

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>where __S: Serializer,

Serialize this value into the given Serde serializer.

impl Support<MvGaussian> for NormalInvWishart

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

Returns true if x is in the support of the Rv

impl StructuralPartialEq for NormalInvWishart