pub struct Gamma { /* private fields */ }
Expand description
Gamma distribution G(α, β) over x in (0, ∞).
NOTE: The gamma distribution is parameterized in terms of shape, α, and rate, β.
β^α
f(x|α, β) = ---- x^(α-1) e^(-βx)
Γ(α)
Implementations§
source§impl Gamma
impl Gamma
sourcepub fn new(shape: f64, rate: f64) -> Result<Self, GammaError>
pub fn new(shape: f64, rate: f64) -> Result<Self, GammaError>
Create a new Gamma
distribution with shape (α) and rate (β).
sourcepub fn new_unchecked(shape: f64, rate: f64) -> Self
pub fn new_unchecked(shape: f64, rate: f64) -> Self
Creates a new Gamma without checking whether the parameters are valid.
sourcepub fn shape(&self) -> f64
pub fn shape(&self) -> f64
Get the shape parameter
§Example
let gam = Gamma::new(2.0, 1.0).unwrap();
assert_eq!(gam.shape(), 2.0);
sourcepub fn set_shape(&mut self, shape: f64) -> Result<(), GammaError>
pub fn set_shape(&mut self, shape: f64) -> Result<(), GammaError>
Set the shape parameter
§Example
let mut gam = Gamma::new(2.0, 1.0).unwrap();
assert_eq!(gam.shape(), 2.0);
gam.set_shape(1.1).unwrap();
assert_eq!(gam.shape(), 1.1);
Will error for invalid values
assert!(gam.set_shape(1.1).is_ok());
assert!(gam.set_shape(0.0).is_err());
assert!(gam.set_shape(-1.0).is_err());
assert!(gam.set_shape(std::f64::INFINITY).is_err());
assert!(gam.set_shape(std::f64::NEG_INFINITY).is_err());
assert!(gam.set_shape(std::f64::NAN).is_err());
sourcepub fn set_shape_unchecked(&mut self, shape: f64)
pub fn set_shape_unchecked(&mut self, shape: f64)
Set the shape parameter without input validation
sourcepub fn rate(&self) -> f64
pub fn rate(&self) -> f64
Get the rate parameter
§Example
let gam = Gamma::new(2.0, 1.0).unwrap();
assert_eq!(gam.rate(), 1.0);
sourcepub fn set_rate(&mut self, rate: f64) -> Result<(), GammaError>
pub fn set_rate(&mut self, rate: f64) -> Result<(), GammaError>
Set the rate parameter
§Example
let mut gam = Gamma::new(2.0, 1.0).unwrap();
assert_eq!(gam.rate(), 1.0);
gam.set_rate(1.1).unwrap();
assert_eq!(gam.rate(), 1.1);
Will error for invalid values
assert!(gam.set_rate(1.1).is_ok());
assert!(gam.set_rate(0.0).is_err());
assert!(gam.set_rate(-1.0).is_err());
assert!(gam.set_rate(std::f64::INFINITY).is_err());
assert!(gam.set_rate(std::f64::NEG_INFINITY).is_err());
assert!(gam.set_rate(std::f64::NAN).is_err());
sourcepub fn set_rate_unchecked(&mut self, rate: f64)
pub fn set_rate_unchecked(&mut self, rate: f64)
Set the rate parameter without input validation
Trait Implementations§
source§impl ConjugatePrior<u16, Poisson> for Gamma
impl ConjugatePrior<u16, Poisson> for Gamma
source§fn posterior(&self, x: &DataOrSuffStat<'_, u16, Poisson>) -> Self
fn posterior(&self, x: &DataOrSuffStat<'_, u16, Poisson>) -> 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<'_, u16, Poisson>
) -> f64
fn ln_m_with_cache( &self, cache: &Self::LnMCache, x: &DataOrSuffStat<'_, u16, Poisson> ) -> f64
Log marginal likelihood with supplied cache.
source§fn ln_pp_cache(&self, x: &DataOrSuffStat<'_, u16, Poisson>) -> Self::LnPpCache
fn ln_pp_cache(&self, x: &DataOrSuffStat<'_, u16, Poisson>) -> 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: &u16) -> f64
fn ln_pp_with_cache(&self, cache: &Self::LnPpCache, y: &u16) -> 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 ConjugatePrior<u32, Poisson> for Gamma
impl ConjugatePrior<u32, Poisson> for Gamma
source§fn posterior(&self, x: &DataOrSuffStat<'_, u32, Poisson>) -> Self
fn posterior(&self, x: &DataOrSuffStat<'_, u32, Poisson>) -> 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<'_, u32, Poisson>
) -> f64
fn ln_m_with_cache( &self, cache: &Self::LnMCache, x: &DataOrSuffStat<'_, u32, Poisson> ) -> f64
Log marginal likelihood with supplied cache.
source§fn ln_pp_cache(&self, x: &DataOrSuffStat<'_, u32, Poisson>) -> Self::LnPpCache
fn ln_pp_cache(&self, x: &DataOrSuffStat<'_, u32, Poisson>) -> 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: &u32) -> f64
fn ln_pp_with_cache(&self, cache: &Self::LnPpCache, y: &u32) -> 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 ConjugatePrior<u8, Poisson> for Gamma
impl ConjugatePrior<u8, Poisson> for Gamma
source§fn posterior(&self, x: &DataOrSuffStat<'_, u8, Poisson>) -> Self
fn posterior(&self, x: &DataOrSuffStat<'_, u8, Poisson>) -> 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<'_, u8, Poisson>
) -> f64
fn ln_m_with_cache( &self, cache: &Self::LnMCache, x: &DataOrSuffStat<'_, u8, Poisson> ) -> f64
Log marginal likelihood with supplied cache.
source§fn ln_pp_cache(&self, x: &DataOrSuffStat<'_, u8, Poisson>) -> Self::LnPpCache
fn ln_pp_cache(&self, x: &DataOrSuffStat<'_, u8, Poisson>) -> 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: &u8) -> f64
fn ln_pp_with_cache(&self, cache: &Self::LnPpCache, y: &u8) -> 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<Poisson> for Gamma
impl ContinuousDistr<Poisson> for Gamma
source§impl ContinuousDistr<f32> for Gamma
impl ContinuousDistr<f32> for Gamma
source§impl ContinuousDistr<f64> for Gamma
impl ContinuousDistr<f64> for Gamma
source§impl<'de> Deserialize<'de> for Gamma
impl<'de> Deserialize<'de> for Gamma
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 PartialEq for Gamma
impl PartialEq for Gamma
source§impl Rv<f32> for Gamma
impl Rv<f32> for Gamma
source§impl Rv<f64> for Gamma
impl Rv<f64> for Gamma
Auto Trait Implementations§
impl !Freeze for Gamma
impl RefUnwindSafe for Gamma
impl Send for Gamma
impl Sync for Gamma
impl Unpin for Gamma
impl UnwindSafe for Gamma
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
source§impl<Fx> Rv<Datum> for Fxwhere
Fx: RvDatum,
impl<Fx> Rv<Datum> for Fxwhere
Fx: RvDatum,
source§impl<SS, SP> SupersetOf<SS> for SPwhere
SS: SubsetOf<SP>,
impl<SS, SP> SupersetOf<SS> for SPwhere
SS: SubsetOf<SP>,
source§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 moresource§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).source§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.source§fn from_subset(element: &SS) -> SP
fn from_subset(element: &SS) -> SP
The inclusion map: converts
self
to the equivalent element of its superset.