Benjamin Deonovic

You can get it to work with an arbitrary covariance matrix using the following code: ``` julia using Mamba ## Model Specification model = Model( x = Stochastic(2, (mu, N,...

If you look at your function `f` you will see that it basically creates an array of length `N` similar to how ``` julia Int64[5 for i in 1:10] ```...

Thanks @mohamed82008, could you point me towards some material for how I can now use the defined custom distribution and bijector in order to sample from the posterior distribution?

Currently getting this error: ``` julia> result = sample(grm(y, n, m, k), MH(), 10) ERROR: MethodError: no method matching reconstruct(::Array{MyDistribution,1}, ::Array{Float64,1}) ``` If I try another sampler (like `HMC`) it...

Thanks a lot @mohamed82008! I've simplified the code a bit (and allowed the categories to be variable for each item. The following code is a bit shorter. Only problem is...

I just tried it....I tabbed over to read about something else and when I looked back the sampling was already done! I'm rerunning it out of disbelief.

One more thing; For the bijector, if I wanted `MyDistribution` to take in as another parameter a "base" distribution ```julia struct MyDistribution

The bijector definition makes sense to me, but I don't understand how to make the inverse work ```julia struct OrderedNormalBijector

Sorry, I'm not sure I'm following. I think I understand what you mean by parameterizing the bijector (ie something like) ```julia struct OrderedNormalBijector{T

I would love to as soon as I get everything sorted out and looking nice :) I've never seen this `orig` notation, where can I read more about this? BTW...