I have a Bayesian model that fails to converge.

Here is the model. A 1-cmt model with proportional error and `LogNormal`

priors and likelihood:

```
@model begin
@param begin
tvcl ~ LogNormal(log(1.1), 0.25)
tvvc ~ LogNormal(log(70), 0.25)
σ ~ truncated(Cauchy(0, 5), 0, Inf)
C ~ LKJCholesky(2, 1.0)
ω ~ Constrained(
MvNormal(zeros(2), Diagonal(0.4^2 * ones(2)));
lower=zeros(2),
upper=fill(Inf, 2),
init=ones(2)
)
end
@random begin
η ~ MvNormal(Diagonal(ω) * C * Diagonal(ω))
end
@pre begin
# PK parameters
CL = tvcl * exp(η[1])
Vc = tvvc * exp(η[2])
end
@dynamics begin
Central' = -CL / Vc * Central
end
@derived begin
cp := @. Central / Vc
dv ~ @. LogNormal(log(cp), cp * σ)
end
end
```

This is my current fit using `BayesMCMC()`

:

```
Chains MCMC chain (1000×6×4 Array{Float64, 3}):
Iterations = 1:1:1000
Number of chains = 4
Samples per chain = 1000
Wall duration = 411.02 seconds
Compute duration = 799.76 seconds
parameters = tvcl, tvvc, σ, C₂,₁, ω₁, ω₂
Summary Statistics
parameters mean std naive_se mcse ess rhat ⋯
Symbol Float64 Float64 Float64 Float64 Float64 Float64 ⋯
tvcl 0.9790 0.0180 0.0003 0.0023 8.0160 5873614936937.1377 ⋯
tvvc 10.1971 0.1832 0.0029 0.0230 8.0160 98493110057788.4062 ⋯
σ 0.3003 0.0007 0.0000 0.0001 8.0160 6062788037191.0049 ⋯
C₂,₁ -0.0012 0.0014 0.0000 0.0002 8.0160 Inf ⋯
ω₁ 0.0901 0.0003 0.0000 0.0000 8.0160 6510201446863.5693 ⋯
ω₂ 0.0898 0.0008 0.0000 0.0001 8.0160 19313243996740.9648 ⋯
```

Take a look at the `rhat`

s they are huge.