diff --git a/EpiAware/src/EpiLatentModels/models/HierarchicalNormal.jl b/EpiAware/src/EpiLatentModels/models/HierarchicalNormal.jl
index 08d55e10b..0d50f9c97 100644
--- a/EpiAware/src/EpiLatentModels/models/HierarchicalNormal.jl
+++ b/EpiAware/src/EpiLatentModels/models/HierarchicalNormal.jl
@@ -6,6 +6,7 @@ The `HierarchicalNormal` struct represents a non-centered hierarchical normal di
 - `HierarchicalNormal(mean, std_prior)`: Constructs a `HierarchicalNormal` instance with the specified mean and standard deviation prior.
 - `HierarchicalNormal(; mean = 0.0, std_prior = truncated(Normal(0,0.1), 0, Inf))`: Constructs a `HierarchicalNormal` instance with the specified mean and standard deviation prior using named arguments and with default values.
 - `HierarchicalNormal(std_prior)`: Constructs a `HierarchicalNormal` instance with the specified standard deviation prior.
+- `HierarchicalNormal(mean, std_prior)`: Constructs a `HierarchicalNormal` instance with the specified mean and standard deviation prior.
 
 ## Examples
 
@@ -43,6 +44,10 @@ function HierarchicalNormal(std_prior::Distribution)
     return HierarchicalNormal(; std_prior = std_prior)
 end
 
+function HierarchicalNormal(mean::Real, std_prior::Distribution)
+    return HierarchicalNormal(mean, std_prior, mean != 0)
+end
+
 @doc raw"
     function EpiAwareBase.generate_latent(obs_model::HierarchicalNormal, n)
 
diff --git a/EpiAware/test/EpiLatentModels/models/HierarchicalNormal.jl b/EpiAware/test/EpiLatentModels/models/HierarchicalNormal.jl
index 1cdb42c02..4f6287447 100644
--- a/EpiAware/test/EpiLatentModels/models/HierarchicalNormal.jl
+++ b/EpiAware/test/EpiLatentModels/models/HierarchicalNormal.jl
@@ -1,6 +1,6 @@
 @testitem "HierarchicalNormal constructor" begin
     using Distributions
-    int = HierarchicalNormal(0.1, truncated(Normal(0, 2), 0, Inf), true)
+    int = HierarchicalNormal(0.1, truncated(Normal(0, 2), 0, Inf))
     @test typeof(int) <: AbstractTuringLatentModel
     @test int.mean == 0.1
     @test int.std_prior == truncated(Normal(0, 2), 0, Inf)
@@ -19,7 +19,7 @@ end
     using HypothesisTests: ExactOneSampleKSTest, pvalue
     using Distributions
 
-    hnorm = HierarchicalNormal(0.2, truncated(Normal(0, 1), 0, Inf), true)
+    hnorm = HierarchicalNormal(0.2, truncated(Normal(0, 1), 0, Inf))
     hnorm_model = generate_latent(hnorm, 10)
     hnorm_model_out = hnorm_model()
     @test length(hnorm_model_out) == 10