Implement missing posterior_optima and likelihood_optima for a test model.

Project.toml

@@ -1,6 +1,6 @@
 name = "DynamicPPL"
 uuid = "366bfd00-2699-11ea-058f-f148b4cae6d8"
-version = "0.23.5"
+version = "0.23.6"
 
 [deps]
 AbstractMCMC = "80f14c24-f653-4e6a-9b94-39d6b0f70001"

src/test_utils.jl

@@ -621,6 +621,13 @@
 function posterior_mean(model::UnivariateAssumeDemoModels)
     return (s=49 / 24, m=7 / 6)
 end
+function likelihood_optima(::DynamicPPL.TestUtils.UnivariateAssumeDemoModels)
+    return (s=1/16, m=7/4)
+end
+function posterior_optima(::DynamicPPL.TestUtils.UnivariateAssumeDemoModels)
+    # TODO: Figure out exact for `s`.
+    return (s=0.907407, m=7/6)
+end
 function Random.rand(
     rng::Random.AbstractRNG, ::Type{NamedTuple}, model::UnivariateAssumeDemoModels
 )
@@ -654,6 +661,31 @@
 
     return vals
 end
+function likelihood_optima(model::DynamicPPL.TestUtils.MultivariateAssumeDemoModels)
+    # Get some containers to fill.
+    vals = Random.rand(model)
+
+    # NOTE: These are "as close to zero as we can get".
+    vals.s[1] = 1e-32
+    vals.s[2] = 1e-32
+
+    vals.m[1] = 1.5
+    vals.m[2] = 2.0
+
+    return vals
+end
+function posterior_optima(model::DynamicPPL.TestUtils.MultivariateAssumeDemoModels)
+    # Get some containers to fill.
+    vals = Random.rand(model)
+
+    # TODO: Figure out exact for `s[1]`.
+    vals.s[1] = 0.890625
+    vals.s[2] = 1
+    vals.m[1] = 3/4
+    vals.m[2] = 1
+
+    return vals
+end
 function Random.rand(
     rng::Random.AbstractRNG, ::Type{NamedTuple}, model::MultivariateAssumeDemoModels
 )
@@ -684,6 +716,31 @@
 
     return vals
 end
+function likelihood_optima(model::DynamicPPL.TestUtils.MatrixvariateAssumeDemoModels)
+    # Get some containers to fill.
+    vals = Random.rand(model)
+
+    # NOTE: These are "as close to zero as we can get".
+    vals.s[1, 1] = 1e-32
+    vals.s[1, 2] = 1e-32
+
+    vals.m[1] = 1.5
+    vals.m[2] = 2.0
+
+    return vals
+end
+function posterior_optima(model::DynamicPPL.TestUtils.MatrixvariateAssumeDemoModels)
+    # Get some containers to fill.
+    vals = Random.rand(model)
+
+    # TODO: Figure out exact for `s[1]`.
+    vals.s[1, 1] = 0.890625
+    vals.s[1, 2] = 1
+    vals.m[1] = 3/4
+    vals.m[2] = 1
+
+    return vals
+end
 function Base.rand(
     rng::Random.AbstractRNG, ::Type{NamedTuple}, model::MatrixvariateAssumeDemoModels
 )