From 5d4e5695dc2c4e93a7e7033a57b0ad26c34808f3 Mon Sep 17 00:00:00 2001
From: Nathanael Bosch <nathanael.bosch@uni-tuebingen.de>
Date: Mon, 30 Oct 2023 11:59:04 +0100
Subject: [PATCH] Add Markov Kernel things to the docs

---
 docs/src/filtering.md  |  8 +++++++-
 test/core/filtering.jl | 28 ----------------------------
 2 files changed, 7 insertions(+), 29 deletions(-)

diff --git a/docs/src/filtering.md b/docs/src/filtering.md
index dfa16399a..783e0eb3c 100644
--- a/docs/src/filtering.md
+++ b/docs/src/filtering.md
@@ -18,5 +18,11 @@ ProbNumDiffEq.update!
 
 ```@docs
 ProbNumDiffEq.smooth
-ProbNumDiffEq.smooth!
+```
+
+## Markov Kernels
+```@docs
+ProbNumDiffEq.AffineNormalKernel
+ProbNumDiffEq.marginalize!
+ProbNumDiffEq.compute_backward_kernel!
 ```
diff --git a/test/core/filtering.jl b/test/core/filtering.jl
index 850121e6d..c1cc3fb98 100644
--- a/test/core/filtering.jl
+++ b/test/core/filtering.jl
@@ -319,34 +319,6 @@ end
             @test m_smoothed ≈ x_out.μ
             @test P_smoothed ≈ Matrix(x_out.Σ)
         end
-        @testset "smooth!" begin
-            _d = d
-            x_curr_psd = Gaussian(m, PSDMatrix(P_R)) |> copy
-            x_next_psd = Gaussian(m_s, PSDMatrix(P_s_R)) |> copy
-            cache = if !KRONECKER
-                (x_pred=copy(x_curr_psd),
-                    G1=zeros(_d, _d),
-                    C_DxD=zeros(_d, _d),
-                    C_2DxD=zeros(2_d, _d),
-                    C_3DxD=zeros(3_d, _d))
-            else
-                (x_pred=copy(x_curr_psd),
-                    G1=IsometricKroneckerProduct(K, zeros(_d, _d)),
-                    C_DxD=IsometricKroneckerProduct(K, zeros(_d, _d)),
-                    C_2DxD=IsometricKroneckerProduct(K, zeros(2_d, _d)),
-                    C_3DxD=IsometricKroneckerProduct(K, zeros(3_d, _d)))
-            end
-            ProbNumDiffEq.smooth!(
-                x_curr_psd,
-                x_next_psd,
-                A,
-                Q_SR,
-                cache,
-            )
-            @test m_smoothed ≈ x_curr_psd.μ
-            @test P_smoothed ≈ Matrix(x_curr_psd.Σ)
-        end
-
         @testset "smooth via backward kernels" begin
             K_forward = ProbNumDiffEq.AffineNormalKernel(copy(A), copy(Q_SR))
             K_backward = ProbNumDiffEq.AffineNormalKernel(