# Sampling using Turing and a customized distribution

I have this customized distribution below:

``````using Distributions
struct OrthoNNDist <: DiscreteMultivariateDistribution
x0::Vector{Int64}
oc::Array{Int64,2}
x1s::Array
prob::Float64
#return a new uniform distribution with all vectors in x1s orthogonal to oc
function OrthoNNDist(x0::Vector{Int}, oc::Array{Int,2})
x1s = []
for i = 1:size(oc)[2]
x1 = x0 + oc[:, i]
if nonneg(x1)
push!(x1s, x1)
end
x1 = x0 - oc[:, i]
if nonneg(x1)
push!(x1s, x1)
end
end
new(x0, oc, x1s, 1.0/length(x1s))
end
end

Base.length(d::OrthoNNDist) = length(d.x0)

Distributions.rand(d::OrthoNNDist) = rand(d.x1s)

Distributions.pdf(d::OrthoNNDist, x::Vector) = x in d.x1s ? d.prob : 0.0
Distributions.pdf(d::OrthoNNDist) = fill(d.prob, size(d.x1s))
Distributions.logpdf(d::OrthoNNDist, x::Vector) = log(pdf(d, x))

``````

and also i have my log_target function that takes a vector as an argument: `log_target(x::Vector) `i don’t think its expression is important, i am struggling here with how can i use the distribution and the target function with Turing i read the doc, but i’m still a bit confused and not sure how to do it i’ll appreciate an example or an explanation of how to do it, i’m still new on Turing, thanks.