Hello,

I’m trying to get my hands on Turing by implementing a Latent Dirichlet Allocation model from a youtube course I’m following. I implemented the following model:

```
using Turing, Distributions
@model function LDA(wordcounts::Matrix{Int})
D, V = size(wordcounts) #100,25
K = 10
W = sum(wordcounts[1,:]) #words per doc (1000 with real data)
π = Matrix{Float64}(undef, D, K)
α = 0.3
for d in 1:D
π[d,:] ~ Dirichlet(K, α)
end
θ = Matrix{Float64}(undef, K, V)
β = 0.3
for k in 1:K
θ[k,:] ~ Dirichlet(V, β)
end
for d in 1:D
p_w = θ'*π[d,:]
wordcounts[d,:] ~ Multinomial(W, p_w)
end
return θ
end
```

The observed variable is a matrix that contains one row per document observed and each row counts the number of occurence of a word in each document. The problem arises with the multinomial variable. When I sample using a random input matrix I get this

```
julia> chain = sample(LDA(rand(1:4, 100, 25)), HMC(0.05,10), 200)
ERROR: MethodError: no method matching Multinomial(::Int64, ::Vector{Any})
Closest candidates are:
Multinomial(::Integer, ::Integer) at C:\Users\Henri\.julia\packages\Distributions\1313k\src\multivariate\multinomial.jl:40
Multinomial(::Integer, ::TV; check_args) where {T<:Real, TV<:AbstractVector{T}} at C:\Users\Henri\.julia\packages\Distributions\1313k\src\multivariate\multinomial.jl:28
Stacktrace:
[1] LDA(__model__::DynamicPPL.Model{typeof(LDA), (:wordcounts,), (), (), Tuple{Matrix{Int64}}, Tuple{}, DynamicPPL.DefaultContext}, __varinfo__::DynamicPPL.ThreadSafeVarInfo{DynamicPPL.UntypedVarInfo{DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distribution}, Vector{AbstractPPL.VarName}, Vector{Real}, Vector{Set{DynamicPPL.Selector}}}, Float64}, Vector{Base.RefValue{Float64}}}, __context__::DynamicPPL.SamplingContext{DynamicPPL.SampleFromUniform, DynamicPPL.DefaultContext, Random._GLOBAL_RNG}, wordcounts::Matrix{Int64})
@ Main c:\Users\Henri\OneDrive - UCL\Doctorat\Cours\Tubinggen Probabilistic ML\Latent Dirichlet\LDAProbML\src\LDAProbML.jl:22
[2] macro expansion
@ C:\Users\Henri\.julia\packages\DynamicPPL\c8MjC\src\model.jl:489 [inlined]
[3] _evaluate!!
@ C:\Users\Henri\.julia\packages\DynamicPPL\c8MjC\src\model.jl:472 [inlined]
[4] evaluate_threadsafe!!(model::DynamicPPL.Model{typeof(LDA), (:wordcounts,), (), (), Tuple{Matrix{Int64}}, Tuple{}, DynamicPPL.DefaultContext}, varinfo::DynamicPPL.UntypedVarInfo{DynamicPPL.Metadata{Dict{AbstractPPL.VarName, Int64}, Vector{Distribution}, Vector{AbstractPPL.VarName}, Vector{Real}, Vector{Set{DynamicPPL.Selector}}}, Float64}, context::DynamicPPL.SamplingContext{DynamicPPL.SampleFromUniform, DynamicPPL.DefaultContext, Random._GLOBAL_RNG})
@ DynamicPPL C:\Users\Henri\.julia\packages\DynamicPPL\c8MjC\src\model.jl:463
[5] evaluate!!
@ C:\Users\Henri\.julia\packages\DynamicPPL\c8MjC\src\model.jl:402 [inlined]
[6] evaluate!!
@ C:\Users\Henri\.julia\packages\DynamicPPL\c8MjC\src\model.jl:415 [inlined]
[7] Model
@ C:\Users\Henri\.julia\packages\DynamicPPL\c8MjC\src\model.jl:377 [inlined]
[8] VarInfo
@ C:\Users\Henri\.julia\packages\DynamicPPL\c8MjC\src\varinfo.jl:127 [inlined]
[9] VarInfo
@ C:\Users\Henri\.julia\packages\DynamicPPL\c8MjC\src\varinfo.jl:126 [inlined]
[10] step(rng::Random._GLOBAL_RNG, model::DynamicPPL.Model{typeof(LDA), (:wordcounts,), (), (), Tuple{Matrix{Int64}}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{HMC{Turing.Core.ForwardDiffAD{40}, (), AdvancedHMC.UnitEuclideanMetric}}; resume_from::Nothing, kwargs::Base.Iterators.Pairs{Union{}, Union{}, Tuple{}, NamedTuple{(), Tuple{}}})
@ DynamicPPL C:\Users\Henri\.julia\packages\DynamicPPL\c8MjC\src\sampler.jl:81
[11] step
@ C:\Users\Henri\.julia\packages\DynamicPPL\c8MjC\src\sampler.jl:74 [inlined]
[12] macro expansion
@ C:\Users\Henri\.julia\packages\AbstractMCMC\BPJCW\src\sample.jl:123 [inlined]
[13] macro expansion
@ C:\Users\Henri\.julia\packages\ProgressLogging\6KXlp\src\ProgressLogging.jl:328 [inlined]
[14] macro expansion
@ C:\Users\Henri\.julia\packages\AbstractMCMC\BPJCW\src\logging.jl:8 [inlined]
[15] mcmcsample(rng::Random._GLOBAL_RNG, model::DynamicPPL.Model{typeof(LDA), (:wordcounts,), (), (), Tuple{Matrix{Int64}}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{HMC{Turing.Core.ForwardDiffAD{40}, (), AdvancedHMC.UnitEuclideanMetric}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, discard_initial::Int64, thinning::Int64, chain_type::Type, kwargs::Base.Iterators.Pairs{Union{}, Union{}, Tuple{}, NamedTuple{(), Tuple{}}})
@ AbstractMCMC C:\Users\Henri\.julia\packages\AbstractMCMC\BPJCW\src\sample.jl:114
[16] sample(rng::Random._GLOBAL_RNG, model::DynamicPPL.Model{typeof(LDA), (:wordcounts,), (), (), Tuple{Matrix{Int64}}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{HMC{Turing.Core.ForwardDiffAD{40}, (), AdvancedHMC.UnitEuclideanMetric}}, N::Int64; chain_type::Type, resume_from::Nothing, progress::Bool, kwargs::Base.Iterators.Pairs{Union{}, Union{}, Tuple{}, NamedTuple{(), Tuple{}}})
@ Turing.Inference C:\Users\Henri\.julia\packages\Turing\Ir2iS\src\inference\Inference.jl:159
[17] sample
@ C:\Users\Henri\.julia\packages\Turing\Ir2iS\src\inference\Inference.jl:158 [inlined]
[18] #sample#2
@ C:\Users\Henri\.julia\packages\Turing\Ir2iS\src\inference\Inference.jl:145 [inlined]
[19] sample
@ C:\Users\Henri\.julia\packages\Turing\Ir2iS\src\inference\Inference.jl:145 [inlined]
[20] #sample#1
@ C:\Users\Henri\.julia\packages\Turing\Ir2iS\src\inference\Inference.jl:135 [inlined]
[21] sample(model::DynamicPPL.Model{typeof(LDA), (:wordcounts,), (), (), Tuple{Matrix{Int64}}, Tuple{}, DynamicPPL.DefaultContext}, alg::HMC{Turing.Core.ForwardDiffAD{40}, (), AdvancedHMC.UnitEuclideanMetric}, N::Int64)
@ Turing.Inference C:\Users\Henri\.julia\packages\Turing\Ir2iS\src\inference\Inference.jl:135
[22] top-level scope
@ REPL[48]:1
```

The model considers that my `p_w`

vector’s eltype is Any. But it’s not, the pi and theta matrices are of `Float64`

numbers and so is `p_w`

when I try in my REPL. I don’t get it, why this error ?