Struct rv::dist::NormalInvChiSquared

pub struct NormalInvChiSquared { /* fields omitted */ }

Prior for Gaussian

Given x ~ N(μ, σ), the Normal Inverse Gamma prior implies that μ ~ N(m, sqrt(v)σ) and ρ ~ InvGamma(a, b).

Implementations

impl NormalInvChiSquared

pub fn new(
    m: f64,
    k: f64,
    v: f64,
    s2: f64
) -> Result<Self, NormalInvChiSquaredError>

Create a new Normal Inverse Gamma distribution

Arguments

• m: The prior mean
• v: Relative variance of μ versus data
• a: The mean of variance is b / (a - 1)
• b: Degrees of freedom of the variance

pub fn new_unchecked(m: f64, k: f64, v: f64, s2: f64) -> Self

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

pub fn params(&self) -> (f64, f64, f64, f64)

Returns (m, k, v, s2)

pub fn m(&self) -> f64

Get the m parameter

pub fn set_m(&mut self, m: f64) -> Result<(), NormalInvChiSquaredError>

Set the value of m

Example

use rv::dist::NormalInvChiSquared;

let mut nix = NormalInvChiSquared::new(0.0, 1.2, 2.3, 3.4).unwrap();
assert_eq!(nix.m(), 0.0);

nix.set_m(-1.1).unwrap();
assert_eq!(nix.m(), -1.1);

Will error for invalid values

assert!(nix.set_m(-1.1).is_ok());
assert!(nix.set_m(std::f64::INFINITY).is_err());
assert!(nix.set_m(std::f64::NEG_INFINITY).is_err());
assert!(nix.set_m(std::f64::NAN).is_err());

pub fn set_m_unchecked(&mut self, m: f64)

Set the value of m without input validation

pub fn k(&self) -> f64

Get the k parameter

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

Set the value of k

Example

use rv::dist::NormalInvChiSquared;

let mut nix = NormalInvChiSquared::new(0.0, 1.2, 2.3, 3.4).unwrap();
assert_eq!(nix.k(), 1.2);

nix.set_k(4.3).unwrap();
assert_eq!(nix.k(), 4.3);

Will error for invalid values

assert!(nix.set_k(2.1).is_ok());

// must be greater than zero
assert!(nix.set_k(0.0).is_err());
assert!(nix.set_k(-1.0).is_err());


assert!(nix.set_k(std::f64::INFINITY).is_err());
assert!(nix.set_k(std::f64::NEG_INFINITY).is_err());
assert!(nix.set_k(std::f64::NAN).is_err());

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

Set the value of k without input validation

pub fn v(&self) -> f64

Get the v parameter

pub fn set_v(&mut self, v: f64) -> Result<(), NormalInvChiSquaredError>

Set the value of v

Example

use rv::dist::NormalInvChiSquared;

let mut nix = NormalInvChiSquared::new(0.0, 1.2, 2.3, 3.4).unwrap();
assert_eq!(nix.v(), 2.3);

nix.set_v(4.3).unwrap();
assert_eq!(nix.v(), 4.3);

Will error for invalid values

assert!(nix.set_v(2.1).is_ok());

// must be greater than zero
assert!(nix.set_v(0.0).is_err());
assert!(nix.set_v(-1.0).is_err());


assert!(nix.set_v(std::f64::INFINITY).is_err());
assert!(nix.set_v(std::f64::NEG_INFINITY).is_err());
assert!(nix.set_v(std::f64::NAN).is_err());

pub fn set_v_unchecked(&mut self, v: f64)

Set the value of v without input validation

pub fn s2(&self) -> f64

Get the s2 parameter

pub fn set_s2(&mut self, s2: f64) -> Result<(), NormalInvChiSquaredError>

Set the value of s2

Example

use rv::dist::NormalInvChiSquared;

let mut nix = NormalInvChiSquared::new(0.0, 1.2, 2.3, 3.4).unwrap();
assert_eq!(nix.s2(), 3.4);

nix.set_s2(4.3).unwrap();
assert_eq!(nix.s2(), 4.3);

Will error for invalid values

assert!(nix.set_s2(2.1).is_ok());

// must be greater than zero
assert!(nix.set_s2(0.0).is_err());
assert!(nix.set_s2(-1.0).is_err());


assert!(nix.set_s2(std::f64::INFINITY).is_err());
assert!(nix.set_s2(std::f64::NEG_INFINITY).is_err());
assert!(nix.set_s2(std::f64::NAN).is_err());

pub fn set_s2_unchecked(&mut self, s2: f64)

Set the value of s2 without input validation

Trait Implementations

impl Clone for NormalInvChiSquared

fn clone(&self) -> NormalInvChiSquared

Returns a copy of the value.

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

Performs copy-assignment from source.

impl ConjugatePrior<f64, Gaussian> for NormalInvChiSquared

type Posterior = Self

Type of the posterior distribution

type LnMCache = f64

Type of the ln_m cache

type LnPpCache = (GaussianSuffStat, f64)

Type of the ln_pp cache

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

Computes the posterior distribution from the data

fn ln_m_cache(&self) -> Self::LnMCache

Compute the cache for the log marginal likelihood.

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

Log marginal likelihood with supplied cache.

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

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

fn ln_pp_with_cache(&self, cache: &Self::LnPpCache, y: &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 Debug for NormalInvChiSquared

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

Formats the value using the given formatter.

impl Display for NormalInvChiSquared

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

Formats the value using the given formatter.

impl GewekeTestable<Gaussian, f64> for NormalInvChiSquared

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

fn update_params<R: Rng>(&self, data: &Vec<f64>, rng: &mut R) -> Gaussian

fn geweke_stats(&self, fx: &Gaussian, xs: &Vec<f64>) -> BTreeMap<String, f64>

impl PartialEq<NormalInvChiSquared> for NormalInvChiSquared

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

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

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

This method tests for !=.

impl Rv<Gaussian> for NormalInvChiSquared

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

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 StructuralPartialEq for NormalInvChiSquared