Hello guys, hope you are doing well. I am trying to implement the US forecasting election model from the paper by Linzer https://www.ime.usp.br/~abe/lista/pdfpWRrt4xFLt.pdf, and it would be great if someone could give me a little help

Here is my implementation:

```
@model function linzer_model(state_polls_dict, hist_state_forecast, poll_dates, states)
n_states = length(hist_state_forecast)
n_dates = length(poll_dates)
σ_δ ~ Uniform(0, 10)
σ_β ~ Uniform(0, 10)
δ = Vector{Real}(undef, n_dates)
β = Array{Real, 2}(undef, n_dates, n_states)
# Random walks of the parameters β and δ
for i in 1:(n_dates)
if i == 1
δ[i] = 0
for j in 1:(n_states)
β[i,j] = logit(hist_state_forecast[j])
end
continue
end
δ[i] ~ Normal(δ[i-1], σ_δ)
for j in 1:(n_states)
β[i,j] ~ Normal(β[i-1,j], σ_β)
end
end
π = Array{Real,2}(undef, n_dates, n_states)
for i in 1:n_dates
π[i,:] = logistic.(β[i,:] .+ δ[i])
end
# rows -> days
# columns -> states
for i in 1:n_states
for j in 1:n_dates
if !(poll_dates[j] in keys(state_polls_dict[states[i]]))
continue
end
n_polls = length(state_polls_dict[states[i]][poll_dates[j]][1,:])
polls = state_polls_dict[states[i]][poll_dates[j]][1,:] # Hillary polls
sample_sizes = state_polls_dict[states[i]][poll_dates[j]][3,:]
for k in 1:n_polls
polls[k] ~ Binomial(sample_sizes[k], π[j,i])
end
end
end
end
```

The model basically goes like this:

- Pre-election polls are generated by a Binomial distribution. We have polls from every state and days before the election.
- the probabilities π for each poll of each states at each time, is given by π_ij = logit−1 (β_ij + δ_j).
- β and δ are obtained by random walks, i.e., β_ij∼ N(βi, j+1, (σ^2)_β), δj ∼ N(δj+1, (σ^2)_δ)
- σ_β and σ_δ have uniform priors.

The arguments of the model are:

- state_polls_dict: A dictionary of dictionaries, containing information about the polls at every state and every date.
- hist_state_forecast: An array with some historical forecasting information of each state
- poll_dates: An array whith all the days where a poll was performed
- states: An array with all the states

The model is quite complex and it would be great if someone could tell me if you see any errors in the implementation or if there is a simpler way to write it. Also, performance tips will be appreciated, as my model is taking too much to run (I am using HMC to sample).

Thanks a lot in advance for your time