Making BayesNets Bayesian

[itsdfish]

Hello,

I would like to use BayesNets.jl with Turing.jl to perform Bayesian parameter estimation. The primary requirement is that a library interacting with Turing is set up generically to support ForwardDiff.Dual types and Float64 in order to allow automatic differentiation to compute gradients. As shown below, I found that DiscreteCPD does not work because it is hardcoded to accept Float64. See this line.

A simple fix is to change that line to

const DiscreteCPD = CategoricalCPD

I tried adding constraints but it threw an error.

const DiscreteCPD = CategoricalCPD{Categorical{T,Vector{T}}} where {T<:Real}

I was wondering if you would be willing to change the line (I could also open a PR), and whether there are additional places that might need to be changed in the code.

Thanks!

MWE

using BayesNets, TikzGraphs
using ForwardDiff

c = ForwardDiff.Dual(.5,.4)
snc = ForwardDiff.Dual(.5,.4)
sc = ForwardDiff.Dual(.5,.4)

bn = BayesNet([DiscreteCPD(:c, [c,(1-c)])])
push!(bn, 
    DiscreteCPD(
        :s,  [:c], [2], 
    [Categorical([snc,(1-snc)]), 
    Categorical([sc,(1-sc)])]
    ),
)

Error

ERROR: TypeError: in typeassert, expected Float64, got a value of type ForwardDiff.Dual{Nothing, Float64, 1}
Stacktrace:
  [1] setindex!(A::Vector{Float64}, x::ForwardDiff.Dual{Nothing, Float64, 1}, i1::Int64)
    @ Base ./array.jl:903
  [2] _unsafe_copyto!(dest::Vector{Float64}, doffs::Int64, src::Vector{ForwardDiff.Dual{Nothing, Float64, 1}}, soffs::Int64, n::Int64)
    @ Base ./array.jl:253
  [3] unsafe_copyto!
    @ ./array.jl:307 [inlined]
  [4] _copyto_impl!
    @ ./array.jl:331 [inlined]
  [5] copyto!
    @ ./array.jl:317 [inlined]
  [6] copyto!
    @ ./array.jl:343 [inlined]
  [7] copyto_axcheck!
    @ ./abstractarray.jl:1104 [inlined]
  [8] Vector{Float64}(x::Vector{ForwardDiff.Dual{Nothing, Float64, 1}})
    @ Base ./array.jl:563
  [9] convert
    @ ./array.jl:554 [inlined]
 [10] convert
    @ ~/.julia/packages/Distributions/1PkiH/src/univariate/discrete/discretenonparametric.jl:45 [inlined]
 [11] setindex!
    @ ./array.jl:903 [inlined]
 [12] _unsafe_copyto!(dest::Vector{Categorical{Float64, Vector{Float64}}}, doffs::Int64, src::Vector{Categorical{ForwardDiff.Dual{Nothing, Float64, 1}, Vector{ForwardDiff.Dual{Nothing, Float64, 1}}}}, soffs::Int64, n::Int64)
    @ Base ./array.jl:253
 [13] unsafe_copyto!
    @ ./array.jl:307 [inlined]
 [14] _copyto_impl!
    @ ./array.jl:331 [inlined]
 [15] copyto!
    @ ./array.jl:317 [inlined]
 [16] copyto!
    @ ./array.jl:343 [inlined]
 [17] copyto_axcheck!
    @ ./abstractarray.jl:1104 [inlined]
 [18] Vector{Categorical{Float64, Vector{Float64}}}(x::Vector{Categorical{ForwardDiff.Dual{Nothing, Float64, 1}, Vector{ForwardDiff.Dual{Nothing, Float64, 1}}}})
    @ Base ./array.jl:563
 [19] convert
    @ ./array.jl:554 [inlined]
 [20] DiscreteCPD(target::Symbol, parents::Vector{Symbol}, parental_ncategories::Vector{Int64}, distributions::Vector{Categorical{ForwardDiff.Dual{Nothing, Float64, 1}, Vector{ForwardDiff.Dual{Nothing, Float64, 1}}}})
    @ BayesNets.CPDs ~/.julia/packages/BayesNets/hZr0W/src/CPDs/categorical_cpd.jl:22
 [21] top-level scope
    @ Untitled-3:13

[mykelk]

That would be awesome! We certainly would welcome a PR!

[mykelk]

Please also add some tests to the test script as well. Thanks!