lp -> logjoint

Author: penelopeysm

tutorials/bayesian-neural-networks/index.qmd

@@ -227,7 +227,7 @@ nn_forward(x, θ) = nn(x, vector_to_parameters(θ, ps))
 fig = plot_data()
 
 # Find the index that provided the highest log posterior in the chain.
-_, i = findmax(ch[:lp])
+_, i = findmax(ch[:logjoint])
 
 # Extract the max row value from i.
 i = i.I[1]
@@ -286,4 +286,4 @@ anim = @gif for i in 1:n_end
 end every 5
 ```
 
-This has been an introduction to the applications of Turing and Lux in defining Bayesian neural networks.
+This has been an introduction to the applications of Turing and Lux in defining Bayesian neural networks.