Struct MultivariateDensity 

pub struct MultivariateDensity<T, D>(/* private fields */)
where
    T: Scalar,
    D: Dim,
    DefaultAllocator: Allocator<D>;

A D-dimensional distribution where each dimension is independent with potentially different univariate distributions. This is a product distribution:

• Each marginal follows one of the available univariate distributions (Normal, Uniform, Cosine, etc.)
• The joint density is the product of marginals: f(x₁, …, xₐ) = f₁(x₁) × … × fₐ(xₐ)

Construction & Examples

Create a mixed 3D distribution (Normal × Uniform × Constant):

let marginals = SVector::from([
    NormalDensity::new(0.0, 1.0, None, None).unwrap().into(),
    UniformDensity::new(-1.0, 1.0).unwrap().into(),
    ConstantDensity::new(2.0).into(),
]);
let _dist = MultivariateDensity::<f64, Const<3>>::new(marginals);

Create a 5D distribution with mixed univariates:

let mvpdf = MultivariateDensity::<f64, Const<5>>::new(SVector::from([
   ConstantDensity::new(1.0).into(),
   CosineDensity::new(0.1, 0.2).unwrap().into(),
   LogUniformDensity::new(0.1, 0.5).unwrap().into(),
   NormalDensity::new(0.1, 0.25, Some(-0.5), Some(1.5)).unwrap().into(),
   UniformDensity::new(1.0, 2.0).unwrap().into(),
]));

Evaluate density at a point:

let marginals = SVector::from([
    NormalDensity::new(0.0, 1.0, None, None).unwrap().into(),
    UniformDensity::new(-1.0, 1.0).unwrap().into(),
]);
let dist = MultivariateDensity::<f64, U2>::new(marginals);
let sample = SVector::from([0.0, 0.5]);
// Use the Density trait to evaluate - see crate::Density for usage patterns
if let Some(dens) = (&dist).density::<U1, U2>(&sample.as_view()) {
    println!("Joint density: {}", dens);
}

Sample from the distribution:

let marginals = SVector::from([
    NormalDensity::new(0.0, 1.0, None, None).unwrap().into(),
    UniformDensity::new(-1.0, 1.0).unwrap().into(),
]);
let dist = MultivariateDensity::<f64, U2>::new(marginals);
let mut rng = StdRng::seed_from_u64(42);
if let Some(sample) = (&dist).sample(&mut rng, &SamplingMode::default()) {
    println!("Generated sample: {:?}", sample);
}

Implementations

impl<T, D> MultivariateDensity<T, D>

where T: RealField, D: Dim, DefaultAllocator: Allocator<D>,

pub fn new(domains: OVector<UnivariateDensity<T>, D>) -> Self

Create a new MultivariateDensity from a vector of UnivariateDensitys.

pub fn marginals(&self) -> &OVector<UnivariateDensity<T>, D>

Return a reference to the underlying vector of UnivariateDensitys.

Trait Implementations

impl<T, D> Clone for MultivariateDensity<T, D>

where T: Scalar + Clone, D: Dim + Clone, DefaultAllocator: Allocator<D>,

fn clone(&self) -> MultivariateDensity<T, D>

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<T, D> Debug for MultivariateDensity<T, D>

where T: Scalar + Debug, D: Dim + Debug, DefaultAllocator: Allocator<D>,

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

Formats the value using the given formatter.

impl<T, D> Density<T, D> for MultivariateDensity<T, D>

where T: RealField + SampleUniform, D: Dim, StandardNormal: Distribution<T>, DefaultAllocator: Allocator<D>,

fn density<RStride: Dim, CStride: Dim>( &self, sample: &VectorView<'_, T, D, RStride, CStride>, ) -> Option<T>

Calculates, or estimates, a density value for a sample. Returns None if the sample is outside of the function domain.

fn domain(&self) -> Domain<T, D>

Returns the underlying function Domain.

fn mean(&self) -> OVector<T, D>

Returns the mean of the distribution.

fn sample( &self, rng: &mut impl RngExt, mode: &SamplingMode, ) -> Option<OVector<T, D>>

Draw a random sample from the probability density distribution using the provided random number generator and sampling mode.

fn sample_iter( &self, rng: &mut impl RngExt, ) -> impl Iterator<Item = Option<OVector<T, D>>>

Returns an iterator that yields random samples from the distribution according to the specified sampling mode.

fn variance(&self) -> OVector<T, D>

Returns the variance of the distribution.

fn ndims(&self) -> usize

where DefaultAllocator: Allocator<D>,

Returns the number of dimensions of the distribution.

impl<'de, T, D> Deserialize<'de> for MultivariateDensity<T, D>

where T: Scalar, D: Dim, DefaultAllocator: Allocator<D>, OVector<UnivariateDensity<T>, D>: Deserialize<'de>,

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

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl<T> From<MultivariateDensity<T, Const<1>>> for UnivariateDensity<T>

where T: RealField,

fn from(mv: MultivariateDensity<T, U1>) -> Self

Convert a 1D MultivariateDensity to a UnivariateDensity by extracting its single marginal.

impl From<MultivariateDensity<f64, Dyn>> for PyMultivariate

fn from(dist: MultivariateDensity<f64, Dyn>) -> Self

Convert a MultivariateDensity with runtime dimensions to a PyMultivariate.

impl<'__deriveMoreLifetime, T, D> IntoIterator for &'__deriveMoreLifetime MultivariateDensity<T, D>

where &'__deriveMoreLifetime OVector<UnivariateDensity<T>, D>: IntoIterator, T: Scalar, D: Dim, DefaultAllocator: Allocator<D>,

type Item = <&'__deriveMoreLifetime Matrix<UnivariateDensity<T>, D, Const<1>, <DefaultAllocator as Allocator<D>>::Buffer<UnivariateDensity<T>>> as IntoIterator>::Item

The type of the elements being iterated over.

type IntoIter = <&'__deriveMoreLifetime Matrix<UnivariateDensity<T>, D, Const<1>, <DefaultAllocator as Allocator<D>>::Buffer<UnivariateDensity<T>>> as IntoIterator>::IntoIter

Which kind of iterator are we turning this into?

fn into_iter(self) -> Self::IntoIter

Creates an iterator from a value.

impl<T, D> IntoIterator for MultivariateDensity<T, D>

where OVector<UnivariateDensity<T>, D>: IntoIterator, T: Scalar, D: Dim, DefaultAllocator: Allocator<D>,

type Item = <Matrix<UnivariateDensity<T>, D, Const<1>, <DefaultAllocator as Allocator<D>>::Buffer<UnivariateDensity<T>>> as IntoIterator>::Item

The type of the elements being iterated over.

type IntoIter = <Matrix<UnivariateDensity<T>, D, Const<1>, <DefaultAllocator as Allocator<D>>::Buffer<UnivariateDensity<T>>> as IntoIterator>::IntoIter

Which kind of iterator are we turning this into?

fn into_iter(self) -> Self::IntoIter

Creates an iterator from a value.

impl<T, D> Serialize for MultivariateDensity<T, D>

where T: Scalar, D: Dim, DefaultAllocator: Allocator<D>, OVector<UnivariateDensity<T>, D>: Serialize,

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

where __S: Serializer,

Serialize this value into the given Serde serializer.