Multivariate students T distribution

src/distribution/mod.rs

@@ -27,6 +27,7 @@ pub use self::laplace::Laplace;
 pub use self::log_normal::LogNormal;
 pub use self::multinomial::Multinomial;
 pub use self::multivariate_normal::MultivariateNormal;
+pub use self::multivariate_students_t::MultivariateStudent;
 pub use self::negative_binomial::NegativeBinomial;
 pub use self::normal::Normal;
 pub use self::pareto::Pareto;
@@ -60,6 +61,7 @@ mod laplace;
 mod log_normal;
 mod multinomial;
 mod multivariate_normal;
+mod multivariate_students_t;
 mod negative_binomial;
 mod normal;
 mod pareto;

src/distribution/multivariate_normal.rs

@@ -1,5 +1,5 @@
 use crate::distribution::Continuous;
-use crate::distribution::Normal;
+use crate::distribution::{MultivariateStudent, Normal};
 use crate::statistics::{Max, MeanN, Min, Mode, VarianceN};
 use crate::{Result, StatsError};
 use nalgebra::{Cholesky, Const, DMatrix, DVector, Dim, DimMin, Dyn, OMatrix, OVector};
@@ -49,6 +49,25 @@ impl MultivariateNormal<Dyn> {
         let cov = DMatrix::from_vec(mean.len(), mean.len(), cov);
         MultivariateNormal::new_from_nalgebra(mean, cov)
     }
+
+    /// Constructs a new multivariate normal distribution from a
+    /// multivariate students t distribution, which have equal variables
+    /// when `mvs.freedom == f64::INFINITY`
+    pub fn from_students(mvs: MultivariateStudent) -> Result<Self> {
+        let mu = mvs.location();
+        let scale = mvs.scale();
+        let cov_det = scale.determinant();
+        let pdf_const = ((2. * PI).powi(mu.nrows() as i32) * cov_det.abs())
+            .recip()
+            .sqrt();
+        Ok(MultivariateNormal {
+            cov_chol_decomp: mvs.scale_chol_decomp(),
+            mu: mvs.location(),
+            cov: mvs.scale(),
+            precision: mvs.precision(),
+            pdf_const,
+        })
+    }
 }
 
 impl<D> MultivariateNormal<D>
@@ -609,4 +628,11 @@ mod tests  {
             ln_pdf(dvector![100., 100.]),
         );
     }
+
+    #[test]
+    #[should_panic]
+    fn test_pdf_mismatched_arg_size() {
+        let mvn = MultivariateNormal::new(vec![0., 0.], vec![1., 0., 0., 1.,]).unwrap();
+        mvn.pdf(vec![1.]); // x.size != mu.size
+    }
 }