diff --git a/src/experimental/ProbabilisticGraphicalModels/bayesnet.jl b/src/experimental/ProbabilisticGraphicalModels/bayesnet.jl
index 076d7a671..dc51241bd 100644
--- a/src/experimental/ProbabilisticGraphicalModels/bayesnet.jl
+++ b/src/experimental/ProbabilisticGraphicalModels/bayesnet.jl
@@ -166,10 +166,28 @@ Ancestral sampling works by:
 2. Sampling from each node in order, using the already-sampled parent values for conditional distributions
 """
 function ancestral_sampling(bn::BayesianNetwork{V}) where {V}
-    ordered_vertices = Graphs.topological_sort(bn.graph)
-    println(ordered_vertices)
+    ordered_vertices = Graphs.topological_sort_by_dfs(bn.graph)
     samples = Dict{V,Any}()
 
+    for vertex_id in ordered_vertices
+        vertex_name = bn.names[vertex_id]
+        
+        if bn.is_observed[vertex_id]
+            samples[vertex_name] = bn.values[vertex_name]
+            continue
+        end
+        
+        if bn.is_stochastic[vertex_id]
+            dist_idx = findfirst(id -> id == vertex_id, bn.stochastic_ids)
+            samples[vertex_name] = rand(bn.distributions[dist_idx])
+        else
+            # deterministic node
+            parent_ids = Graphs.inneighbors(bn.graph, vertex_id)
+            parent_values = [samples[bn.names[pid]] for pid in parent_ids]
+            func_idx = findfirst(id -> id == vertex_id, bn.deterministic_ids)
+            samples[vertex_name] = bn.deterministic_functions[func_idx](parent_values...)
+        end
+    end
 
     return samples
 end
diff --git a/test/experimental/ProbabilisticGraphicalModels/bayesnet.jl b/test/experimental/ProbabilisticGraphicalModels/bayesnet.jl
index b98f8b036..12825dad0 100644
--- a/test/experimental/ProbabilisticGraphicalModels/bayesnet.jl
+++ b/test/experimental/ProbabilisticGraphicalModels/bayesnet.jl
@@ -97,33 +97,25 @@ using JuliaBUGS.ProbabilisticGraphicalModels:
     end
 
     @testset "Simple ancestral sampling" begin
-        # Create a Bayesian network
         bn = BayesianNetwork{Symbol}()
-    
-        # Add stochastic vertices with their distributions
-        add_stochastic_vertex!(bn, :A, Normal(0, 1), false)  # Stochastic variable A
-        add_stochastic_vertex!(bn, :B, Normal(0, 1), false)  # Stochastic variable B
-        add_stochastic_vertex!(bn, :C, Normal(0, 1), false)  # Stochastic variable C
-    
-        # Add edges to define relationships
-        add_edge!(bn, :A, :B)  # A -> B
-        add_edge!(bn, :A, :C)  # A -> C
-    
-        # Perform ancestral sampling
+        
+        # Add stochastic vertices
+        add_stochastic_vertex!(bn, :A, Normal(0, 1), false)
+        add_stochastic_vertex!(bn, :B, Normal(1, 2), false)
+        
+        # Add deterministic vertex C = A + B
+        add_deterministic_vertex!(bn, :C, (a, b) -> a + b)
+        add_edge!(bn, :A, :C)
+        add_edge!(bn, :B, :C)
+        
         samples = ancestral_sampling(bn)
-    
-        # Debugging: Print samples to see its contents
-        println("Samples: ", samples)
-    
-        # Check if all sampled variables are present
+        
         @test haskey(samples, :A)
         @test haskey(samples, :B)
         @test haskey(samples, :C)
-    
-        # Check if the values are numerical since we are sampling from Normal distributions
-        @test isa(samples[:A], Number)
-        @test isa(samples[:B], Number)
-        @test isa(samples[:C], Number)
+        @test samples[:A] isa Number
+        @test samples[:B] isa Number
+        @test samples[:C] ≈ samples[:A] + samples[:B]
     end