Struct rv::dist::NormalGamma
source · pub struct NormalGamma { /* private fields */ }
Expand description
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)
.
Implementations§
source§impl NormalGamma
impl NormalGamma
sourcepub fn new(m: f64, r: f64, s: f64, v: f64) -> Result<Self, NormalGammaError>
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
sourcepub fn new_unchecked(m: f64, r: f64, s: f64, v: f64) -> Self
pub fn new_unchecked(m: f64, r: f64, s: f64, v: f64) -> Self
Creates a new NormalGamma without checking whether the parameters are valid.
sourcepub fn set_m(&mut self, m: f64) -> Result<(), NormalGammaError>
pub fn set_m(&mut self, m: f64) -> Result<(), NormalGammaError>
Set the value of m
Example
use rv::dist::NormalGamma;
let mut ng = NormalGamma::new(0.0, 1.2, 2.3, 3.4).unwrap();
assert_eq!(ng.m(), 0.0);
ng.set_m(-1.1).unwrap();
assert_eq!(ng.m(), -1.1);
Will error for invalid values
assert!(ng.set_m(-1.1).is_ok());
assert!(ng.set_m(std::f64::INFINITY).is_err());
assert!(ng.set_m(std::f64::NEG_INFINITY).is_err());
assert!(ng.set_m(std::f64::NAN).is_err());
sourcepub fn set_m_unchecked(&mut self, m: f64)
pub fn set_m_unchecked(&mut self, m: f64)
Set the value of m without input validation
sourcepub fn set_r(&mut self, r: f64) -> Result<(), NormalGammaError>
pub fn set_r(&mut self, r: f64) -> Result<(), NormalGammaError>
Set the value of r
Example
use rv::dist::NormalGamma;
let mut ng = NormalGamma::new(0.0, 1.2, 2.3, 3.4).unwrap();
assert_eq!(ng.r(), 1.2);
ng.set_r(2.1).unwrap();
assert_eq!(ng.r(), 2.1);
Will error for invalid values
assert!(ng.set_r(2.1).is_ok());
// must be greater than zero
assert!(ng.set_r(0.0).is_err());
assert!(ng.set_r(-1.0).is_err());
assert!(ng.set_r(std::f64::INFINITY).is_err());
assert!(ng.set_r(std::f64::NEG_INFINITY).is_err());
assert!(ng.set_r(std::f64::NAN).is_err());
sourcepub fn set_r_unchecked(&mut self, r: f64)
pub fn set_r_unchecked(&mut self, r: f64)
Set the value of r without input validation
sourcepub fn set_s(&mut self, s: f64) -> Result<(), NormalGammaError>
pub fn set_s(&mut self, s: f64) -> Result<(), NormalGammaError>
Set the value of s
Example
use rv::dist::NormalGamma;
let mut ng = NormalGamma::new(0.0, 1.2, 2.3, 3.4).unwrap();
assert_eq!(ng.s(), 2.3);
ng.set_s(3.2).unwrap();
assert_eq!(ng.s(), 3.2);
Will error for invalid values
assert!(ng.set_s(2.1).is_ok());
// must be greater than zero
assert!(ng.set_s(0.0).is_err());
assert!(ng.set_s(-1.0).is_err());
assert!(ng.set_s(std::f64::INFINITY).is_err());
assert!(ng.set_s(std::f64::NEG_INFINITY).is_err());
assert!(ng.set_s(std::f64::NAN).is_err());
sourcepub fn set_s_unchecked(&mut self, s: f64)
pub fn set_s_unchecked(&mut self, s: f64)
Set the value of s without input validation
sourcepub fn set_v(&mut self, v: f64) -> Result<(), NormalGammaError>
pub fn set_v(&mut self, v: f64) -> Result<(), NormalGammaError>
Set the value of v
Example
use rv::dist::NormalGamma;
let mut ng = NormalGamma::new(0.0, 1.2, 2.3, 3.4).unwrap();
assert_eq!(ng.v(), 3.4);
ng.set_v(4.3).unwrap();
assert_eq!(ng.v(), 4.3);
Will error for invalid values
assert!(ng.set_v(2.1).is_ok());
// must be greater than zero
assert!(ng.set_v(0.0).is_err());
assert!(ng.set_v(-1.0).is_err());
assert!(ng.set_v(std::f64::INFINITY).is_err());
assert!(ng.set_v(std::f64::NEG_INFINITY).is_err());
assert!(ng.set_v(std::f64::NAN).is_err());
sourcepub fn set_v_unchecked(&mut self, v: f64)
pub fn set_v_unchecked(&mut self, v: f64)
Set the value of v without input validation
Trait Implementations§
source§impl Clone for NormalGamma
impl Clone for NormalGamma
source§fn clone(&self) -> NormalGamma
fn clone(&self) -> NormalGamma
Returns a copy of the value. Read more
1.0.0 · source§fn clone_from(&mut self, source: &Self)
fn clone_from(&mut self, source: &Self)
Performs copy-assignment from
source
. Read moresource§impl ConjugatePrior<f64, Gaussian> for NormalGamma
impl ConjugatePrior<f64, Gaussian> for NormalGamma
§type Posterior = NormalGamma
type Posterior = NormalGamma
Type of the posterior distribution
§type LnPpCache = (GaussianSuffStat, f64)
type LnPpCache = (GaussianSuffStat, f64)
Type of the
ln_pp
cachesource§fn posterior(&self, x: &DataOrSuffStat<'_, f64, Gaussian>) -> Self
fn posterior(&self, x: &DataOrSuffStat<'_, f64, Gaussian>) -> Self
Computes the posterior distribution from the data
source§fn ln_m_cache(&self) -> Self::LnMCache
fn ln_m_cache(&self) -> Self::LnMCache
Compute the cache for the log marginal likelihood.
source§fn ln_m_with_cache(
&self,
cache: &Self::LnMCache,
x: &DataOrSuffStat<'_, f64, Gaussian>
) -> f64
fn ln_m_with_cache( &self, cache: &Self::LnMCache, x: &DataOrSuffStat<'_, f64, Gaussian> ) -> f64
Log marginal likelihood with supplied cache.
source§fn ln_pp_cache(&self, x: &DataOrSuffStat<'_, f64, Gaussian>) -> Self::LnPpCache
fn ln_pp_cache(&self, x: &DataOrSuffStat<'_, f64, Gaussian>) -> Self::LnPpCache
Compute the cache for the Log posterior predictive of y given x. Read more
source§fn ln_pp_with_cache(&self, cache: &Self::LnPpCache, y: &f64) -> f64
fn ln_pp_with_cache(&self, cache: &Self::LnPpCache, y: &f64) -> f64
Log posterior predictive of y given x with supplied ln(norm)
source§fn ln_m(&self, x: &DataOrSuffStat<'_, X, Fx>) -> f64
fn ln_m(&self, x: &DataOrSuffStat<'_, X, Fx>) -> f64
The log marginal likelihood
source§fn ln_pp(&self, y: &X, x: &DataOrSuffStat<'_, X, Fx>) -> f64
fn ln_pp(&self, y: &X, x: &DataOrSuffStat<'_, X, Fx>) -> f64
Log posterior predictive of y given x
source§fn m(&self, x: &DataOrSuffStat<'_, X, Fx>) -> f64
fn m(&self, x: &DataOrSuffStat<'_, X, Fx>) -> f64
Marginal likelihood of x
source§impl ContinuousDistr<Gaussian> for NormalGamma
impl ContinuousDistr<Gaussian> for NormalGamma
source§impl Debug for NormalGamma
impl Debug for NormalGamma
source§impl<'de> Deserialize<'de> for NormalGamma
impl<'de> Deserialize<'de> for NormalGamma
source§fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>where
__D: Deserializer<'de>,
fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>where __D: Deserializer<'de>,
Deserialize this value from the given Serde deserializer. Read more
source§impl Display for NormalGamma
impl Display for NormalGamma
source§impl From<&NormalGamma> for String
impl From<&NormalGamma> for String
source§fn from(ng: &NormalGamma) -> String
fn from(ng: &NormalGamma) -> String
Converts to this type from the input type.
source§impl GewekeTestable<Gaussian, f64> for NormalGamma
impl GewekeTestable<Gaussian, f64> for NormalGamma
source§impl PartialEq<NormalGamma> for NormalGamma
impl PartialEq<NormalGamma> for NormalGamma
source§fn eq(&self, other: &NormalGamma) -> bool
fn eq(&self, other: &NormalGamma) -> bool
This method tests for
self
and other
values to be equal, and is used
by ==
.source§impl Rv<Gaussian> for NormalGamma
impl Rv<Gaussian> for NormalGamma
source§impl Serialize for NormalGamma
impl Serialize for NormalGamma
source§impl Support<Gaussian> for NormalGamma
impl Support<Gaussian> for NormalGamma
impl StructuralPartialEq for NormalGamma
Auto Trait Implementations§
impl RefUnwindSafe for NormalGamma
impl Send for NormalGamma
impl Sync for NormalGamma
impl Unpin for NormalGamma
impl UnwindSafe for NormalGamma
Blanket Implementations§
source§impl<T> BorrowMut<T> for Twhere
T: ?Sized,
impl<T> BorrowMut<T> for Twhere T: ?Sized,
source§fn borrow_mut(&mut self) -> &mut T
fn borrow_mut(&mut self) -> &mut T
Mutably borrows from an owned value. Read more
§impl<SS, SP> SupersetOf<SS> for SPwhere
SS: SubsetOf<SP>,
impl<SS, SP> SupersetOf<SS> for SPwhere SS: SubsetOf<SP>,
§fn to_subset(&self) -> Option<SS>
fn to_subset(&self) -> Option<SS>
The inverse inclusion map: attempts to construct
self
from the equivalent element of its
superset. Read more§fn is_in_subset(&self) -> bool
fn is_in_subset(&self) -> bool
Checks if
self
is actually part of its subset T
(and can be converted to it).§fn to_subset_unchecked(&self) -> SS
fn to_subset_unchecked(&self) -> SS
Use with care! Same as
self.to_subset
but without any property checks. Always succeeds.§fn from_subset(element: &SS) -> SP
fn from_subset(element: &SS) -> SP
The inclusion map: converts
self
to the equivalent element of its superset.