diff --git a/mlpp_lib/probabilistic_layers.py b/mlpp_lib/probabilistic_layers.py
index 6a005d1..3928b47 100644
--- a/mlpp_lib/probabilistic_layers.py
+++ b/mlpp_lib/probabilistic_layers.py
@@ -361,6 +361,36 @@ def _sample_n(self, n, seed=None):
 
                     return chosen_samples
 
+                def _mean(self):
+                    """
+                    Original: X ~ N(mu, sigma)
+                    Censored: Y = X if 0 <= X <= 1 else 0 if X < 0 else 1
+
+                    Law of total expectations: E[Y] = E[Y | X > 1] * P(X > 1) + E[Y | X < 0] * P(X < 0) + E[Y | 0 <= X <= 1] * P(0 <= X <= 1)
+                                                    = P(X > 1) * 1 + P(X < 0) * 0 + E[X | 0 <= X <= 1] * P(0 <= X <= 1)
+                                                    = 1 - Phi((1 - mu) / sigma) + E[Z ~ TruncNormal(mu, sigma, 0, 1)] * (Phi((1 - mu) / sigma) - Phi(-mu / sigma))
+                    Ref for TruncatedNormal mean: https://en.wikipedia.org/wiki/Truncated_normal_distribution
+                    """
+                    original_mean = self.normal.mean()
+                    low_bound_standard = (0 - original_mean) / self.normal.stddev()
+                    high_bound_standard = (1 - original_mean) / self.normal.stddev()
+
+                    self.low_bound_cdf = self.normal.cdf(low_bound_standard)
+                    self.high_bound_cdf = self.normal.cdf(high_bound_standard)
+
+                    self.low_bound_pdf = self.normal.prob(low_bound_standard)
+                    self.high_bound_pdf = self.normal.prob(high_bound_standard)
+
+                    return original_mean + self.normal.stddev() * (
+                        self.low_bound_pdf - self.high_bound_pdf
+                    ) / (self.high_bound_cdf - self.low_bound_cdf + 1e-3)
+
+                def _log_prob(self, value):
+                    original_log_prob = self.normal.log_prob(value)
+
+                    return original_log_prob - tf.math.log(
+                        self.high_bound_cdf - self.low_bound_cdf + 1e-3
+                    )
 
             return independent_lib.Independent(
                 CustomCensored(normal_dist),