How to create sum-log-likelihood of custom distribution for use in Turing.jl

[Dan]

I think what you need is to define a multivariate distribution and then a custom logpdf for the whole vector.

This might be relevant:

Custom multivariate distribution in Turing.jl

I want to use a custom multivariate distribution that actually generates arrays, and I want to know if there’s anything I need to modify in order to be adapted to be used in Turing using Distributions struct OrthoNNDist <: DiscreteMultivariateDistribution x0::Vector{Float64} oc::Array{Float64,2} x1s::Array prob::Float64 #return a new uniform distribution with all vectors in x1s orthogonal to oc function OrthoNNDist(x0::Vector{Float64}, oc::Array{Float64,2}) x1s = [] for i = 1:size(…