diff --git a/src/bayesian/negativebinomial_regression.jl b/src/bayesian/negativebinomial_regression.jl
index 20638ab..e2f8a47 100644
--- a/src/bayesian/negativebinomial_regression.jl
+++ b/src/bayesian/negativebinomial_regression.jl
@@ -16,7 +16,7 @@ end
 
 """
 ```julia
-fit(formula::FormulaTerm, data::DataFrame, modelClass::NegBinomRegression, prior::Prior_Ridge, h::Float64 = 0.1, sim_size::Int64 = 1000)
+fit(formula::FormulaTerm, data::DataFrame, modelClass::NegBinomRegression, prior::Prior_Ridge, h::Float64 = 1.0, sim_size::Int64 = 1000)
 ```
 
 Fit a Bayesian Negative Binomial Regression model on the input data with a Ridge prior.
@@ -103,7 +103,7 @@ function fit(
     data::DataFrame,
     modelClass::NegBinomRegression,
     prior::Prior_Ridge,
-    h::Float64 = 0.1,
+    h::Float64 = 1.0,
     sim_size::Int64 = 1000
 )
     @model NegativeBinomialRegression(X, y) = begin
@@ -218,7 +218,7 @@ function fit(
     data::DataFrame,
     modelClass::NegBinomRegression,
     prior::Prior_Laplace,
-    h::Float64 = 0.1,
+    h::Float64 = 1.0,
     sim_size::Int64 = 1000
 )
     @model NegativeBinomialRegression(X, y) = begin
@@ -511,6 +511,7 @@ function fit(
     data::DataFrame,
     modelClass::NegBinomRegression,
     prior::Prior_HorseShoe,
+    h::Float64 = 1.0,
     sim_size::Int64 = 1000
 )
     @model NegativeBinomialRegression(X, y) = begin
@@ -523,11 +524,11 @@ function fit(
         
         τ ~ halfcauchy    ## Global Shrinkage
         λ ~ filldist(halfcauchy, p) ## Local Shrinkage
-        σ ~ halfcauchy
+        σ ~ InverseGamma(h, h)
         #α ~ Normal(0, τ * σ)
         β0 = repeat([0], p)  ## prior mean
-        β ~ MvNormal(β0, λ * τ *σ)
-
+        # β ~ MvNormal(β0, λ * τ *σ)
+        β ~ MvNormal(β0, λ * τ)
 
         ## link
         #z = α .+ X * β
diff --git a/test/numerical/bayesian/LogisticRegression.jl b/test/numerical/bayesian/LogisticRegression.jl
index 07aa246..470d66b 100644
--- a/test/numerical/bayesian/LogisticRegression.jl
+++ b/test/numerical/bayesian/LogisticRegression.jl
@@ -40,8 +40,8 @@ tests = [
     (
         Prior_HorseShoe(),
         (
-            (Logit(), 0.38683395333332327),
-            (Probit(), 0.38253233489484173),
+            (Logit(), 0.7599999999740501),
+            (Probit(), 0.7580564600751047),
             (Cloglog(), 0.7667553778881738),
             (Cauchit(), 0.7706755564626601)
         )