Hi all,

I am trying to replicate a Likelihood Approximation Network (LAN) with Flux.jl. LANs are used to learn the likelihood function of intractable computational models. As a proof of concept, I am trying to apply the method to two simple models for which the likelihood function is known: a Gaussian model and a decision model called the Linear Ballistic Accumulator (LBA). I was successful in developing a LAN for the Gaussian model, but the LAN for the LBA produces NaNs as predictions. I tried various solutions from other threads, such as decreasing the learning rate and using `BatchNorm`

, but those recommendations did not solve the problem. Changing the activation function to `relu`

, solved the `NaN`

problem, but interfered with the ability of the NN to learn the likelihood function.

Can I do anything to fix this problem? Please let me know if there are more details I can provide.