format

src/MarkovChainMonteCarlo.jl

@@ -157,22 +157,23 @@ function EmulatorPosteriorModel(
 ) where {FT <: AbstractFloat}
     return AdvancedMH.DensityModel(
         function (θ)
-        # θ: model params we evaluate at; in original coords.
-        # transform_to_real = false means g, g_cov, obs_sample are in decorrelated coords.
-        #
-        # Recall predict() written to return multiple N_samples: expects input to be a 
-        # Matrix with N_samples columns. Returned g is likewise a Matrix, and g_cov is a
-        # Vector of N_samples covariance matrices. For MH, N_samples is always 1, so we 
-        # have to reshape()/re-cast input/output; simpler to do here than add a 
-        # predict() method.
-        g, g_cov = Emulators.predict(em, reshape(θ, :, 1), transform_to_real = false)
-        if isa(g_cov[1],Real)
-            return logpdf(MvNormal(obs_sample, g_cov[1]*I), vec(g)) + get_logpdf(prior, θ)
-        else
-            return logpdf(MvNormal(obs_sample, g_cov[1]), vec(g)) + get_logpdf(prior, θ)
-        end
-
-        end)
+            # θ: model params we evaluate at; in original coords.
+            # transform_to_real = false means g, g_cov, obs_sample are in decorrelated coords.
+            #
+            # Recall predict() written to return multiple N_samples: expects input to be a 
+            # Matrix with N_samples columns. Returned g is likewise a Matrix, and g_cov is a
+            # Vector of N_samples covariance matrices. For MH, N_samples is always 1, so we 
+            # have to reshape()/re-cast input/output; simpler to do here than add a 
+            # predict() method.
+            g, g_cov = Emulators.predict(em, reshape(θ, :, 1), transform_to_real = false)
+            if isa(g_cov[1], Real)
+                return logpdf(MvNormal(obs_sample, g_cov[1] * I), vec(g)) + get_logpdf(prior, θ)
+            else
+                return logpdf(MvNormal(obs_sample, g_cov[1]), vec(g)) + get_logpdf(prior, θ)
+            end
+
+        end,
+    )
 end
 
 # ------------------------------------------------------------------------------------------