make test in mix for multi normal with matrix inputs

stan-dev · Jul 20, 2021 · 0a293e4 · 0a293e4
1 parent cc3f2f5
commit 0a293e4
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Showing 2 changed files with 17 additions and 4 deletions.
diff --git a/stan/math/prim/prob/multi_normal_lpdf.hpp b/stan/math/prim/prob/multi_normal_lpdf.hpp
@@ -109,8 +109,6 @@ return_type_t<T_y, T_loc, T_covar> multi_normal_lpdf(const T_y& y,
                                                      const T_loc& mu,
                                                      const T_covar& Sigma) {
   using T_covar_elem = typename scalar_type<T_covar>::type;
-  using lp_type = return_type_t<T_y, T_loc, T_covar>;
-  using Eigen::Dynamic;
   static const char* function = "multi_normal_lpdf";
   check_positive(function, "Covariance matrix rows", Sigma.rows());
   auto&& Sigma_ref = to_ref(Sigma);
@@ -125,7 +123,7 @@ return_type_t<T_y, T_loc, T_covar> multi_normal_lpdf(const T_y& y,
                   "columns of location parameter", mu.cols());
   check_size_match(function, "Columns of random variable", y.cols(),
                   "rows of covariance parameter", Sigma.rows());
-  lp_type lp(0.0);
+  return_type_t<T_y, T_loc, T_covar> lp(0.0);
   auto&& y_ref = to_ref(y);
   auto&& mu_ref = to_ref(mu);
   const Eigen::Index size_y = y_ref.cols();
@@ -146,7 +144,6 @@ return_type_t<T_y, T_loc, T_covar> multi_normal_lpdf(const T_y& y,
   }
 
   if (include_summand<propto, T_y, T_loc, T_covar_elem>::value) {
-   lp_type sum_lp_vec(0.0);
    lp -= 0.5 * trace_inv_quad_form_ldlt(ldlt_Sigma, (y_ref - mu_ref).transpose());
   }
   return lp;

diff --git a/test/unit/math/mix/prob/multi_normal_test.cpp b/test/unit/math/mix/prob/multi_normal_test.cpp
@@ -47,6 +47,22 @@ TEST(ProbDistributionsMultiNormal, matvar) {
   stan::test::expect_ad_matvar(f, y1, mu1, Sigma00);
 }
 
+TEST(ProbDistributionsMultiNormal, matrix) {
+  auto f = [](const auto& y, const auto& mu, const auto& sigma) {
+    auto sigma_sym = stan::math::multiply(0.5, sigma + sigma.transpose());
+    return stan::math::multi_normal_lpdf(y, mu, sigma_sym);
+  };
+
+  Eigen::MatrixXd y(2, 3);
+  y << 2.0, -2.0, 11.0, 2.0, -2.0, 11.0;
+  Eigen::MatrixXd mu(2, 3);
+  mu << 1.0, -1.0, 3.0, 1.0, -1.0, 3.0;
+  Eigen::MatrixXd Sigma(3, 3);
+  Sigma << 9.0, -3.0, 0.0, -3.0, 4.0, 0.0, 0.0, 0.0, 5.0;
+  stan::test::expect_ad(f, y, mu, Sigma);
+  stan::test::expect_ad_matvar(f, y, mu, Sigma);
+}
+
 TEST(ProbDistributionsMultiNormal, fvar_var) {
   using Eigen::Dynamic;
   using Eigen::Matrix;