diff --git a/pyrecest/distributions/cart_prod/se2_bingham_distribution.py b/pyrecest/distributions/cart_prod/se2_bingham_distribution.py
new file mode 100644
index 000000000..64901c6dc
--- /dev/null
+++ b/pyrecest/distributions/cart_prod/se2_bingham_distribution.py
@@ -0,0 +1,52 @@
+import numpy as np
+from numpy.linalg import pinv, eig
+from scipy.stats import multivariate_normal
+from pyrecest.distributions.cart_prod.abstract_se2_distribution import AbstractSE2Distribution
+
+
+class SE2BinghamDistribution(AbstractSE2Distribution):
+
+    def __init__(self, C1, C2, C3):
+        self.C1 = C1
+        self.C2 = C2
+        self.C3 = C3
+
+    def computeNC(self):
+        # Compute normalization constant of the distribution.
+        pass
+
+    def mode(self):
+        # Computes one of the modes of the distribution.
+        pass
+
+    def sampleDeterministic(self):
+        # Generates deterministic samples.
+        pass
+
+    def sample(self, n=10000):
+        # Samples from current distribution.
+        pass
+
+    def plotState(self, scalingFactor=1, circleColor=None, angleColor=None, samplesForMatching=10000):
+        if circleColor is None:
+            circleColor = np.array([0, 0.4470, 0.7410])
+        if angleColor is None:
+            angleColor = np.array([0.8500, 0.3250, 0.0980])
+        pass
+
+    def getBinghamMarginal(self):
+        # Computes Bingham marginal of circular part.
+        BM = self.C1 - (self.C2.T @ pinv(self.C3) @ self.C2)
+        M, Z = eig(BM)
+        order = np.argsort(np.diag(Z), axis=-1)
+        Z = Z - Z[2]
+        M = M[:, order]
+        b = BinghamDistribution(Z, M)
+        return b
+
+    @staticmethod
+    def fit(samples, weights=None):
+        if weights is None:
+            weights = np.ones((1, samples.shape[1])) / samples.shape[1]
+        # Estimates parameters of SE2 distribution from samples.
+        pass
\ No newline at end of file