Struct rv::dist::NormalInvWishart

pub struct NormalInvWishart { /* fields omitted */ }

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);

Methods

impl NormalInvWishart

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

A scale factor on Σ, κ₀

pub fn set_df(&mut self, val: usize) -> &mut Self

The degrees of freedom, ν > |μ| - 1

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 dims(&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 df(&self) -> usize

Get the degrees of freedom, df

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, Dynamic, U1, VecStorage<f64, Dynamic, U1>>, MvGaussian> for NormalInvWishart

type Posterior = NormalInvWishart

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

Computes the posterior distribution from the data

fn ln_m(&self, x: &DataOrSuffStat<'a, DVector<f64>, MvGaussian>) -> f64

Log marginal likelihood

fn ln_pp(
    &self,
    y: &DVector<f64>,
    x: &DataOrSuffStat<'a, DVector<f64>, MvGaussian>
) -> 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 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

Performs the conversion.

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: &NormalInvWishart) -> bool

This method tests for !=.

impl PartialOrd<NormalInvWishart> for NormalInvWishart

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

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

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

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

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

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

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

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

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

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

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

impl StructuralPartialEq for NormalInvWishart

impl Support<MvGaussian> for NormalInvWishart

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

Returns true if x is in the support of the Rv