Struct rv::dist::InvWishart

pub struct InvWishart { /* private fields */ }

Inverse Wishart distribution, W^-1(Ψ,ν) over positive definite matrices.

Implementations

impl InvWishart

pub fn new(inv_scale: DMatrix<f64>, df: usize) -> Result<Self, InvWishartError>

Create an Inverse Wishart distribution, W^-1(Ψ,ν) with p-by-p inverse scale matrix, Ψ, and degrees of freedom, ν > p - 1.

Arguments

• inv_scale: p-dimensional inverse scale matrix, Ψ
• df: Degrees of freedom, ν > p - 1

pub fn new_unchecked(inv_scale: DMatrix<f64>, df: usize) -> Self

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

pub fn identity(dims: usize) -> Self

Create an Inverse Wishart distribution, W^-1(I^p, p)

pub fn ndims(&self) -> usize

pub fn inv_scale(&self) -> &DMatrix<f64>

Get a reference to the inverse scale parameter

pub fn df(&self) -> usize

Get the degrees of freedom

pub fn set_df(&mut self, df: usize) -> Result<(), InvWishartError>

Set the value of df

pub fn set_df_unchecked(&mut self, df: usize)

Set the value of df without input validation

pub fn set_inv_scale( &mut self, inv_scale: DMatrix<f64> ) -> Result<(), InvWishartError>

Set inverse scale parameter

pub fn set_inv_scale_unchecked(&mut self, inv_scale: DMatrix<f64>)

Trait Implementations

impl Clone for InvWishart

fn clone(&self) -> InvWishart

Returns a copy of the value.

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

Performs copy-assignment from source.

impl ContinuousDistr<Matrix<f64, Dyn, Dyn, VecStorage<f64, Dyn, Dyn>>> for InvWishart

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 InvWishart

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

Formats the value using the given formatter.

impl<'de> Deserialize<'de> for InvWishart

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

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl Mean<Matrix<f64, Dyn, Dyn, VecStorage<f64, Dyn, Dyn>>> for InvWishart

fn mean(&self) -> Option<DMatrix<f64>>

Returns None if the mean is undefined

impl Mode<Matrix<f64, Dyn, Dyn, VecStorage<f64, Dyn, Dyn>>> for InvWishart

fn mode(&self) -> Option<DMatrix<f64>>

Returns None if the mode is undefined or is not a single value

impl PartialEq for InvWishart

fn eq(&self, other: &InvWishart) -> 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<Matrix<f64, Dyn, Dyn, VecStorage<f64, Dyn, Dyn>>> for InvWishart

fn ln_f(&self, x: &DMatrix<f64>) -> f64

Probability function

fn draw<R: Rng>(&self, rng: &mut R) -> DMatrix<f64>

Single draw from the Rv

fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<DMatrix<f64>>

Multiple draws of the Rv

fn f(&self, x: &X) -> f64

Probability function

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 InvWishart

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

where __S: Serializer,

Serialize this value into the given Serde serializer.

impl Support<Matrix<f64, Dyn, Dyn, VecStorage<f64, Dyn, Dyn>>> for InvWishart

fn supports(&self, x: &DMatrix<f64>) -> bool

Returns true if x is in the support of the Rv

impl StructuralPartialEq for InvWishart