diff --git a/examples/hybrid_imaging.jl b/examples/hybrid_imaging.jl
index 173e632b..954e2a5e 100644
--- a/examples/hybrid_imaging.jl
+++ b/examples/hybrid_imaging.jl
@@ -44,7 +44,7 @@ obs = ehtim.obsdata.load_uvfits(joinpath(dirname(pathof(Comrade)), "..", "exampl
 # Now we do some minor preprocessing:
 #   - Scan average the data since the data have been preprocessed so that the gain phases
 #      coherent.
-obs = scan_average(obs).add_fractional_noise(0.01)
+obs = scan_average(obs).add_fractional_noise(0.02)
 
 # For this tutorial we will once again fit complex visibilities since they
 # provide the most information once the telescope/instrument model are taken
@@ -109,7 +109,7 @@ end
 
 # Now let's define our metadata. First we will define the cache for the image. This is
 # required to compute the numerical Fourier transform.
-fovxy  = μas2rad(150.0)
+fovxy  = μas2rad(250.0)
 npix   = 32
 grid   = imagepixels(fovxy, fovxy, npix, npix)
 buffer = IntensityMap(zeros(npix,npix), grid)
@@ -221,7 +221,7 @@ post = Posterior(lklhd, prior)
 xrand = prior_sample(rng, post)
 # and then plot the results
 import WGLMakie as CM
-g = imagepixels(μas2rad(150.0), μas2rad(150.0), 128, 128)
+g = imagepixels(μas2rad(250.0), μas2rad(250.0), 128, 128)
 imageviz(intensitymap(skymodel(post, xrand), g))
 
 # ## Reconstructing the Image
diff --git a/examples/imaging_closures.jl b/examples/imaging_closures.jl
index 2b938b20..41d4cd4e 100644
--- a/examples/imaging_closures.jl
+++ b/examples/imaging_closures.jl
@@ -123,9 +123,9 @@ end
 # Gaussian Markov random fields are extremly flexible models.
 # To prevent overfitting it is common to use priors that penalize complexity. Therefore, we
 # want to use priors that enforce similarity to our mean image, and prefer smoothness.
-cprior = HierarchicalPrior(fmap, Uniform(0.0, 1_000.0))
+cprior = HierarchicalPrior(fmap, InverseGamma(1.0, -log(0.01*rat)))
 
-prior = NamedDist(c = cprior, σimg = Uniform(0.0, 5.0), fg=Uniform(0.0, 1.0))
+prior = NamedDist(c = cprior, σimg = truncated(Normal(0.0, 0.1); lower = 0.0), fg=Uniform(0.0, 1.0))
 
 lklhd = RadioLikelihood(sky, dlcamp, dcphase;
                         skymeta = metadata)
diff --git a/examples/imaging_pol.jl b/examples/imaging_pol.jl
index 1768083d..55e42f81 100644
--- a/examples/imaging_pol.jl
+++ b/examples/imaging_pol.jl
@@ -102,7 +102,7 @@ Page(exportable=true, offline=true) # hide
 
 # For reproducibility we use a stable random number genreator
 using StableRNGs
-rng = StableRNG(123)
+rng = StableRNG(125)
 
 
 # Now we will load some synthetic polarized data.
@@ -303,7 +303,7 @@ using OptimizationOptimJL
 using Zygote
 f = OptimizationFunction(tpost, Optimization.AutoZygote())
 ℓ = logdensityof(tpost)
-prob = Optimization.OptimizationProblem(f, prior_sample(tpost), nothing)
+prob = Optimization.OptimizationProblem(f, prior_sample(rng, tpost), nothing)
 sol = solve(prob, LBFGS(), maxiters=15_000, g_tol=1e-1);
 
 # !!! warning
@@ -329,17 +329,18 @@ img = intensitymap!(copy(imgtruesub), skymodel(post, xopt))
 
 #Plotting the results gives
 import WGLMakie as CM
-fig = CM.Figure(;resolution=(450, 200));
+fig = CM.Figure(;resolution=(450, 350));
 polimage(fig[1,1], imgtruesub,
                    axis=(xreversed=true, aspect=1, title="Truth", limits=((-20.0,20.0), (-20.0, 20.0))),
-                   length_norm=1, plot_total=true,
-                   pcolorrange=(-0.25, 0.25), pcolormap=CM.Reverse(:jet))
+                   length_norm=1, plot_total=true, pcolormap=:RdBu,
+                   pcolorrange=(-0.25, 0.25),)
 polimage(fig[1,2], img,
                    axis=(xreversed=true, aspect=1, title="Recon.",  limits=((-20.0,20.0), (-20.0, 20.0))),
-                   length_norm=1, plot_total=true,
-                   pcolorrange=(-0.25, 0.25), pcolormap=CM.Reverse(:jet))
-CM.Colorbar(fig[1,3], colormap=CM.Reverse(:jet), colorrange=(-0.25, 0.25), label="Signed Polarization Fraction sign(V)*|p|")
-CM.colgap!(fig.layout, 1)
+                   length_norm=1, plot_total=true, pcolormap=:RdBu,
+                   pcolorrange=(-0.25, 0.25),)
+CM.Colorbar(fig[2,:], colormap=:RdBu, vertical=false, colorrange=(-0.25, 0.25), label="Signed Polarization Fraction sign(V)*|p|", flipaxis=false)
+CM.colgap!(fig.layout, 3)
+CM.rowgap!(fig.layout, 3)
 fig
 #-
 
diff --git a/examples/imaging_vis.jl b/examples/imaging_vis.jl
index 2fbaa998..05c09ea1 100644
--- a/examples/imaging_vis.jl
+++ b/examples/imaging_vis.jl
@@ -24,7 +24,7 @@ using LinearAlgebra
 
 # For reproducibility we use a stable random number genreator
 using StableRNGs
-rng = StableRNG(42)
+rng = StableRNG(123)
 
 
 
@@ -39,7 +39,7 @@ obs = ehtim.obsdata.load_uvfits(joinpath(dirname(pathof(Comrade)), "..", "exampl
 #   - Scan average the data since the data have been preprocessed so that the gain phases
 #      coherent.
 #   - Add 1% systematic noise to deal with calibration issues that cause 1% non-closing errors.
-obs = scan_average(obs.add_fractional_noise(0.01))
+obs = scan_average(obs.add_fractional_noise(0.02))
 
 # Now we extract our complex visibilities.
 dvis = extract_table(obs, ComplexVisibilities())
@@ -206,7 +206,7 @@ end
 # and to prevent overfitting it is common to use priors that penalize complexity. Therefore, we
 # want to use priors that enforce similarity to our mean image. If the data wants more complexity
 # then it will drive us away from the prior.
-cprior = HierarchicalPrior(fmap, Uniform(0.0, 1000.0))#InverseGamma(1.0, -log(0.01*rat)))
+cprior = HierarchicalPrior(fmap, InverseGamma(1.0, -log(0.01*rat)))
 
 
 # We can now form our model parameter priors. Like our other imaging examples, we use a
@@ -357,7 +357,7 @@ imgs = intensitymap.(samples, fovx, fovy, 128,  128)
 
 mimg = mean(imgs)
 simg = std(imgs)
-fig = CM.Figure(;resolution=(800, 800))
+fig = CM.Figure(;resolution=(400, 400))
 CM.image(fig[1,1], mimg,
                    axis=(xreversed=true, aspect=1, title="Mean Image"),
                    colormap=:afmhot)