diff --git a/Project.toml b/Project.toml
index fe116e2e..04597cdd 100644
--- a/Project.toml
+++ b/Project.toml
@@ -1,7 +1,7 @@
 name = "AbstractGPs"
 uuid = "99985d1d-32ba-4be9-9821-2ec096f28918"
 authors = ["JuliaGaussianProcesses Team"]
-version = "0.4.0"
+version = "0.5.0-DEV"
 
 [deps]
 ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
diff --git a/src/latent_gp.jl b/src/latent_gp.jl
index caf27ba7..996b7bff 100644
--- a/src/latent_gp.jl
+++ b/src/latent_gp.jl
@@ -2,8 +2,10 @@
     LatentGP(f<:GP, lik, Σy)
 
  - `f` is a `AbstractGP`.
- - `lik` is the likelihood function which maps samples from `f` to the corresponding
- conditional likelihood distributions (i.e., `lik` must return a `Distribution` compatible with the observations).
+ - `lik` is a function mapping inputs `x` to a likelihood function, itself mapping samples
+    from `f` to the corresponding conditional likelihood distributions (i.e., `lik` must
+    return another function, which returns a `Distribution` compatible with the observations
+    when called at values of the latent process).
  - `Σy` is the noise under which the latent GP is "observed"; this represents the jitter used to avoid numeric instability and should generally be small.
     
 """
@@ -26,7 +28,7 @@ struct LatentFiniteGP{Tfx<:FiniteGP,Tlik}
     lik::Tlik
 end
 
-(lgp::LatentGP)(x) = LatentFiniteGP(lgp.f(x, lgp.Σy), lgp.lik)
+(lgp::LatentGP)(x) = LatentFiniteGP(lgp.f(x, lgp.Σy), lgp.lik(x))
 
 function Distributions.rand(rng::AbstractRNG, lfgp::LatentFiniteGP)
     f = rand(rng, lfgp.fx)
diff --git a/test/latent_gp.jl b/test/latent_gp.jl
index 99ecfa07..e2622c23 100644
--- a/test/latent_gp.jl
+++ b/test/latent_gp.jl
@@ -3,10 +3,9 @@
     x = rand(10)
     y = rand(10)
 
-    lgp = LatentGP(gp, x -> MvNormal(x, 0.1), 1e-5)
+    lgp = LatentGP(gp, x -> (f -> MvNormal(f, 0.1)), 1e-5)
     @test lgp isa LatentGP
     @test lgp.f isa AbstractGPs.AbstractGP
-    @test lgp.Σy isa Real
 
     lfgp = lgp(x)
     @test lfgp isa AbstractGPs.LatentFiniteGP