Struct rv::dist::NormalInvGamma

pub struct NormalInvGamma { /* private fields */ }

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 NormalInvGamma

pub fn new(m: f64, v: f64, a: f64, b: f64) -> Result<Self, NormalInvGammaError>

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, v: f64, a: f64, b: f64) -> Self

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

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

Returns (m, v, a, b)

pub fn m(&self) -> f64

Get the m parameter

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

Set the value of m

Example

use rv::dist::NormalInvGamma;

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

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

Will error for invalid values

assert!(nig.set_m(-1.1).is_ok());
assert!(nig.set_m(std::f64::INFINITY).is_err());
assert!(nig.set_m(std::f64::NEG_INFINITY).is_err());
assert!(nig.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 v(&self) -> f64

Get the v parameter

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

Set the value of v

Example

use rv::dist::NormalInvGamma;

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

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

Will error for invalid values

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

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


assert!(nig.set_v(std::f64::INFINITY).is_err());
assert!(nig.set_v(std::f64::NEG_INFINITY).is_err());
assert!(nig.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 a(&self) -> f64

Get the a parameter

pub fn set_a(&mut self, a: f64) -> Result<(), NormalInvGammaError>

Set the value of a

Example

use rv::dist::NormalInvGamma;

let mut nig = NormalInvGamma::new(0.0, 1.2, 2.3, 3.4).unwrap();
assert_eq!(nig.a(), 2.3);

nig.set_a(4.3).unwrap();
assert_eq!(nig.a(), 4.3);

Will error for invalid values

assert!(nig.set_a(2.1).is_ok());

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


assert!(nig.set_a(std::f64::INFINITY).is_err());
assert!(nig.set_a(std::f64::NEG_INFINITY).is_err());
assert!(nig.set_a(std::f64::NAN).is_err());

pub fn set_a_unchecked(&mut self, a: f64)

Set the value of a without input validation

pub fn b(&self) -> f64

Get the b parameter

pub fn set_b(&mut self, b: f64) -> Result<(), NormalInvGammaError>

Set the value of b

Example

use rv::dist::NormalInvGamma;

let mut nig = NormalInvGamma::new(0.0, 1.2, 2.3, 3.4).unwrap();
assert_eq!(nig.b(), 3.4);

nig.set_b(4.3).unwrap();
assert_eq!(nig.b(), 4.3);

Will error for invalid values

assert!(nig.set_b(2.1).is_ok());

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


assert!(nig.set_b(std::f64::INFINITY).is_err());
assert!(nig.set_b(std::f64::NEG_INFINITY).is_err());
assert!(nig.set_b(std::f64::NAN).is_err());

pub fn set_b_unchecked(&mut self, b: f64)

Set the value of b without input validation

Trait Implementations

impl Clone for NormalInvGamma

fn clone(&self) -> NormalInvGamma

Returns a copy of the value.

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

Performs copy-assignment from source.

impl ConjugatePrior<f64, Gaussian> for NormalInvGamma

type Posterior = NormalInvGamma

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 NormalInvGamma

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

Formats the value using the given formatter.

impl<'de> Deserialize<'de> for NormalInvGamma

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl Display for NormalInvGamma

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

Formats the value using the given formatter.

impl From<&NormalInvGamma> for String

fn from(nig: &NormalInvGamma) -> String

Converts to this type from the input type.

impl GewekeTestable<Gaussian, f64> for NormalInvGamma

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

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

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

impl PartialEq for NormalInvGamma

fn eq(&self, other: &NormalInvGamma) -> 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<Gaussian> for NormalInvGamma

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 Serialize for NormalInvGamma

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

where __S: Serializer,

Serialize this value into the given Serde serializer.

impl StructuralPartialEq for NormalInvGamma