Comments (1)
Since mixture of Dirichilet distributions lives on a simplex, so its bijector has to be a SimplexBijector.
I've defined a custom distribution with a SimplexBijector to solve this error. Similarly one can define a custom distribution with IdentityBijector for mixture of MvNormal distributions.
using Bijectors, Turing, Distributions, Random
struct CustomMixture <: ContinuousMultivariateDistribution
a::Vector{Float64}
b::Vector{Float64}
weights::Vector{Float64}
end
function Base.rand(rng::Random.AbstractRNG, d::CustomMixture)
sample = rand(rng, MixtureModel(Dirichlet, [d.a, d.b], d.weights))
return sample
end
function Distributions.logpdf(d::CustomMixture, x::AbstractVector)
return logpdf(MixtureModel(Dirichlet, [d.a, d.b], d.weights), x)
end
Base.length(d::CustomMixture) = length(d.a)
Bijectors.bijector(d::CustomMixture) = Bijectors.SimplexBijector{1}()
from bijectors.jl.
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from bijectors.jl.