Using truncation in MixtureModel

New to Julia
,
1

Can we also use truncation here?

MixtureModel(Normal[
   Normal(-2.0, 1.2),
   Normal(0.0, 1.0),
   Normal(3.0, 2.5)])

I was trying with some truncated distributions and it gives error. These distributions are obtained from fitting Normal distribution to a vector and then truncate them.

Error:

ERROR: MethodError: Cannot `convert` an object of type 
  Truncated{Normal{Float64}, Continuous, Float64, Float64, Float64} to an object of type 
  Normal

Closest candidates are:
  convert(::Type{Normal}, ::NormalCanon)
   @ Distributions ~/.julia/packages/Distributions/Ufrz2/src/univariate/continuous/normalcanon.jl:34
  convert(::Type{T}, ::T) where T
   @ Base Base.jl:64

Stacktrace:
 [1] setindex!(A::Vector{Normal}, x::Truncated{Normal{Float64}, Continuous, Float64, Float64, Float64}, i1::Int64)
   @ Base ./array.jl:969
 [2] getindex(::Type{Normal}, ::Truncated{Normal{Float64}, Continuous, Float64, Float64, Float64}, ::Truncated{Normal{Float64}, Continuous, Float64, Float64, Float64}, ::Truncated{Normal{Float64}, Continuous, Float64, Float64, Float64}, ::Truncated{Normal{Float64}, Continuous, Float64, Float64, Float64}, ::Vararg{Truncated{Normal{Float64}, Continuous, Float64, Float64, Float64}})
   @ Base ./array.jl:403
 [3] top-level scope
   @ ~/PhD/Program_Julia/code/fitting_dist.jl:25
2

Haven’t tried it, but maybe truncating each component distribution and then creating the mixture model from the truncated distributions is possible? (So, reverse order of the steps you are doing)

3

Works just fine without coercing the vector of components to have element type Normal :slight_smile:

julia> comps = truncated.([
          Normal(-2.0, 1.2),
             Normal(0.0, 1.0),
                Normal(3.0, 2.5)], upper=2.0, lower=-2.0)
3-element Vector{Truncated{Normal{Float64}, Continuous, Float64, Float64, Float64}}:
 Truncated(Normal{Float64}(μ=-2.0, σ=1.2); lower=-2.0, upper=2.0)
 Truncated(Normal{Float64}(μ=0.0, σ=1.0); lower=-2.0, upper=2.0)
 Truncated(Normal{Float64}(μ=3.0, σ=2.5); lower=-2.0, upper=2.0)

julia> MixtureModel(comps)
MixtureModel{Truncated{Normal{Float64}, Continuous, Float64, Float64, Float64}}(K = 3)
components[1] (prior = 0.3333): Truncated(Normal{Float64}(μ=-2.0, σ=1.2); lower=-2.0, upper=2.0)
components[2] (prior = 0.3333): Truncated(Normal{Float64}(μ=0.0, σ=1.0); lower=-2.0, upper=2.0)
components[3] (prior = 0.3333): Truncated(Normal{Float64}(μ=3.0, σ=2.5); lower=-2.0, upper=2.0)
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