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diff --git a/rand/rand_distr/src/dirichlet.rs b/rand/rand_distr/src/dirichlet.rs
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+// Copyright 2018 Developers of the Rand project.
+// Copyright 2013 The Rust Project Developers.
+//
+// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
+// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
+// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
+// option. This file may not be copied, modified, or distributed
+// except according to those terms.
+
+//! The dirichlet distribution.
+
+use rand::Rng;
+use crate::{Distribution, Gamma, StandardNormal, Exp1, Open01};
+use crate::utils::Float;
+
+/// The dirichelet distribution `Dirichlet(alpha)`.
+///
+/// The Dirichlet distribution is a family of continuous multivariate
+/// probability distributions parameterized by a vector alpha of positive reals.
+/// It is a multivariate generalization of the beta distribution.
+///
+/// # Example
+///
+/// ```
+/// use rand::prelude::*;
+/// use rand_distr::Dirichlet;
+///
+/// let dirichlet = Dirichlet::new(vec![1.0, 2.0, 3.0]).unwrap();
+/// let samples = dirichlet.sample(&mut rand::thread_rng());
+/// println!("{:?} is from a Dirichlet([1.0, 2.0, 3.0]) distribution", samples);
+/// ```
+#[derive(Clone, Debug)]
+pub struct Dirichlet<N> {
+    /// Concentration parameters (alpha)
+    alpha: Vec<N>,
+}
+
+/// Error type returned from `Dirchlet::new`.
+#[derive(Clone, Copy, Debug, PartialEq, Eq)]
+pub enum Error {
+    /// `alpha.len() < 2`.
+    AlphaTooShort,
+    /// `alpha <= 0.0` or `nan`.
+    AlphaTooSmall,
+    /// `size < 2`.
+    SizeTooSmall,
+}
+
+impl<N: Float> Dirichlet<N>
+where StandardNormal: Distribution<N>, Exp1: Distribution<N>, Open01: Distribution<N>
+{
+    /// Construct a new `Dirichlet` with the given alpha parameter `alpha`.
+    ///
+    /// Requires `alpha.len() >= 2`.
+    #[inline]
+    pub fn new<V: Into<Vec<N>>>(alpha: V) -> Result<Dirichlet<N>, Error> {
+        let a = alpha.into();
+        if a.len() < 2 {
+            return Err(Error::AlphaTooShort);
+        }
+        for &ai in &a {
+            if !(ai > N::from(0.0)) {
+                return Err(Error::AlphaTooSmall);
+            }
+        }
+
+        Ok(Dirichlet { alpha: a })
+    }
+
+    /// Construct a new `Dirichlet` with the given shape parameter `alpha` and `size`.
+    ///
+    /// Requires `size >= 2`.
+    #[inline]
+    pub fn new_with_size(alpha: N, size: usize) -> Result<Dirichlet<N>, Error> {
+        if !(alpha > N::from(0.0)) {
+            return Err(Error::AlphaTooSmall);
+        }
+        if size < 2 {
+            return Err(Error::SizeTooSmall);
+        }
+        Ok(Dirichlet {
+            alpha: vec![alpha; size],
+        })
+    }
+}
+
+impl<N: Float> Distribution<Vec<N>> for Dirichlet<N>
+where StandardNormal: Distribution<N>, Exp1: Distribution<N>, Open01: Distribution<N>
+{
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> Vec<N> {
+        let n = self.alpha.len();
+        let mut samples = vec![N::from(0.0); n];
+        let mut sum = N::from(0.0);
+
+        for (s, &a) in samples.iter_mut().zip(self.alpha.iter()) {
+            let g = Gamma::new(a, N::from(1.0)).unwrap();
+            *s = g.sample(rng);
+            sum += *s;
+        }
+        let invacc = N::from(1.0) / sum;
+        for s in samples.iter_mut() {
+            *s *= invacc;
+        }
+        samples
+    }
+}
+
+#[cfg(test)]
+mod test {
+    use super::Dirichlet;
+    use crate::Distribution;
+
+    #[test]
+    fn test_dirichlet() {
+        let d = Dirichlet::new(vec![1.0, 2.0, 3.0]).unwrap();
+        let mut rng = crate::test::rng(221);
+        let samples = d.sample(&mut rng);
+        let _: Vec<f64> = samples
+            .into_iter()
+            .map(|x| {
+                assert!(x > 0.0);
+                x
+            })
+            .collect();
+    }
+
+    #[test]
+    fn test_dirichlet_with_param() {
+        let alpha = 0.5f64;
+        let size = 2;
+        let d = Dirichlet::new_with_size(alpha, size).unwrap();
+        let mut rng = crate::test::rng(221);
+        let samples = d.sample(&mut rng);
+        let _: Vec<f64> = samples
+            .into_iter()
+            .map(|x| {
+                assert!(x > 0.0);
+                x
+            })
+            .collect();
+    }
+
+    #[test]
+    #[should_panic]
+    fn test_dirichlet_invalid_length() {
+        Dirichlet::new_with_size(0.5f64, 1).unwrap();
+    }
+
+    #[test]
+    #[should_panic]
+    fn test_dirichlet_invalid_alpha() {
+        Dirichlet::new_with_size(0.0f64, 2).unwrap();
+    }
+}