diff --git a/test/pointwise_logdensities.jl b/test/pointwise_logdensities.jl
index e796c89ef..a422965ef 100644
--- a/test/pointwise_logdensities.jl
+++ b/test/pointwise_logdensities.jl
@@ -52,21 +52,50 @@ end
     model = DynamicPPL.TestUtils.demo_dot_assume_dot_observe()
     # FIXME(torfjelde): Make use of `varname_and_value_leaves` once we've introduced
     # an impl of this for containers.
+    # NOTE(torfjelde): This only returns the varnames of the _random_ variables, i.e. excl. observed.
     vns = DynamicPPL.TestUtils.varnames(model)
     # Get some random `NamedTuple` samples from the prior.
-    vals = [DynamicPPL.TestUtils.rand_prior_true(model) for _ = 1:5]
+    num_iters = 3
+    vals = [DynamicPPL.TestUtils.rand_prior_true(model) for _ in 1:num_iters]
     # Concatenate the vector representations and create a `Chains` from it.
     vals_arr = reduce(hcat, mapreduce(DynamicPPL.tovec, vcat, values(nt)) for nt in vals)
-    chain = Chains(permutedims(vals_arr), map(Symbol, vns));
+    chain = Chains(permutedims(vals_arr), map(Symbol, vns))
+
+    # Compute the different pointwise logdensities.
     logjoints_pointwise = pointwise_logdensities(model, chain)
+    logpriors_pointwise = pointwise_prior_logdensities(model, chain)
+    loglikelihoods_pointwise = pointwise_loglikelihoods(model, chain)
+
+    # Check that they contain the correct variables.
+    @test all(string(vn) in keys(logjoints_pointwise) for vn in vns)
+    @test all(string(vn) in keys(logpriors_pointwise) for vn in vns)
+    @test !any(Base.Fix2(startswith, "x"), keys(logpriors_pointwise))
+    @test !any(string(vn) in keys(loglikelihoods_pointwise) for vn in vns)
+    @test all(Base.Fix2(startswith, "x"), keys(loglikelihoods_pointwise))
+
     # Get the sum of the logjoints for each of the iterations.
     logjoints = [
-        sum(logjoints_pointwise[string(vn)][idx] for vn in vns)
-        for idx = 1:5
+        sum(logjoints_pointwise[vn][idx] for vn in keys(logjoints_pointwise)) for
+        idx in 1:num_iters
+    ]
+    logpriors = [
+        sum(logpriors_pointwise[vn][idx] for vn in keys(logpriors_pointwise)) for
+        idx in 1:num_iters
     ]
-    for (val, logp) in zip(vals, logjoints)
+    loglikelihoods = [
+        sum(loglikelihoods_pointwise[vn][idx] for vn in keys(loglikelihoods_pointwise)) for
+        idx in 1:num_iters
+    ]
+
+    for (val, logjoint, logprior, loglikelihood) in
+        zip(vals, logjoints, logpriors, loglikelihoods)
         # Compare true logjoint with the one obtained from `pointwise_logdensities`.
         logjoint_true = DynamicPPL.TestUtils.logjoint_true(model, val...)
-        @test logp ≈ logjoint_true
+        logprior_true = DynamicPPL.TestUtils.logprior_true(model, val...)
+        loglikelihood_true = DynamicPPL.TestUtils.loglikelihood_true(model, val...)
+
+        @test logjoint ≈ logjoint_true
+        @test logprior ≈ logprior_true
+        @test loglikelihood ≈ loglikelihood_true
     end
-end;
+end