I’m trying to fit a parasite dynamic model to some experimental data using Turing.jl, but I’m having some trouble specifying the prior variance, σ. It works fine if I use an InverseGamma distribution (i.e., resulting parameters seem reasonable), but since the data were drawn from a multinomial distribution, an InverseGamma prior and a MvNormal likelihood seem inappropriate. **My understanding is that I would then use a Dirichlet prior and a multinomial likelihood (?). How would one implement this?** The answer is definitely “learn more about Bayesian statistics”, but getting this working will go a long way toward helping me understand what’s going on without having to second guess my code.

The data are the number of preinfective larvae and infective larvae counted in multiple hosts at different days post-infection. I think the likelihood should be multinomial because at any given day there could be preinfective, infective, and/or dead larvae (although I don’t have data for the latter, so maybe Binomial? I get the same warnings in that case).

My attempts at using Multinomial or Binomial likelihoods result in endless warnings:

```
┌ Warning: The current proposal will be rejected due to numerical error(s).
│ isfinite.((θ, r, ℓπ, ℓκ)) = (true, true, false, true)
└ @ AdvancedHMC C:\Users\alexa\.julia\packages\AdvancedHMC\P9wqk\src\hamiltonian.jl:47
```

without ever getting a result.

The data are not great, but I’m hoping to get some information on development rates and mortality rates for the larvae.

I’m working off the Lotka-Volterra example at https://turing.ml/dev/tutorials/10-bayesiandiffeq/

```
using Turing, Distributions, DifferentialEquations
using MCMCChains, Plots, StatsPlots
#Model of the experimental infection protocol
const t_1 = 5.0
const γ = 1/t_1
function DevDelay_simpODE(du,u,p,t)
#p = [muU, muI, m, tau] = [mortality rate preinfective, mort rate infective, initial # of U, delay period before developing to infectivity (1/development rate)]
if t<t_1
J = p[3]*γ - p[1]*u[1]*u[1] #inject some larvae at very start of experiment
else
J = 0.0
end
if t<p[4]
S = 0.0
else
S = 1.0
end
du[1] = dU = J - p[1]*u[1]*u[1] - γ*u[1]*S #preinfective
du[2] = dI = γ*u[1]*S - p[2]*u[2] #infective
du[3] = dD = p[1]*u[1]*u[1] + p[2]*u[2] #dead
end
#Data (number of U and I in a host on a given day post-infection)
datax11 = [25,25,25,35,35,35,35,35,42,42,42,42,42,49,49,49,49,49,48,48,48,55,55,55,55,55,62,62,62,62,62,69,69,69,69,69,76,76,76,76,76].+5
datayU11 = [7,8,5,7,9,2,8,2,8,14,3,16,4,2,7,5,4,6,3,13,2,4,14,6,12,2,3,2,1,3,5,4,2,3,1,1,1,6,1,3,8]
datayI11 = [0,0,0,0,0,0,0,0,0,9,6,9,8,1,4,0,2,2,0,3,1,7,6,8,4,8,5,14,6,9,11,7,6,5,5,7,4,3,2,5,13]
data_11 = [datayU11 datayI11]'
Turing.setadbackend(:forwarddiff)
@model function fit11_5(data)
tspan11 = (0.0,81.0)
u0 = [0.0;0.0;0.0]
#Priors
# σ ~ InverseGamma(2, 3) #This "works"
σ ~ Dirichlet(ones(2)/2) #But should be this if likelihood is Multinomial?
muU ~ truncated(Normal(0.002,0.002),0.001,0.02)
muI ~ truncated(Normal(0.002,0.002),0.001,0.02)
m ~ truncated(Normal(12.5,5.0),0,90)
tau ~ truncated(Normal(50.0,30.0),10.0,120.0)
p11 = [muU, muI, m, tau]
prob11 = ODEProblem(DevDelay_simpODE,u0,tspan11,p11)
predicted = solve(prob11,Tsit5(),saveat=datax11)
for i = 1:length(predicted)
# data[:,i] ~ MvNormal(predicted[i][1:2], σ) #This "works" with InverseGamma
data[:,i] ~ Multinomial(2, σ) #How do you incorporate σ into likelihood function? Does it act as the probability vector?
end
end
model = fit11_5(data_11)
chain11_5 = sample(model, NUTS(.65),10)
plot(chain11_5)
```

This is a small part of a much more complicated fitting process. I was having some issues with convergence while using MLE, probably because of the small sample sizes. I thought I might have more luck with Bayes since I have some prior information I could incorporate. This is my first foray into Bayesian statistics, so any help would be greatly appreciated.