I am trying to compute the maximum a posterior (MAP) estimate and want to get the hessian of the log likelihood too. This is what I have tried with respect to computing the Hessian:

```
using Flux, Distributions
prior_mean = Normal(178, 20)
prior_sd = Uniform(0, 50)
heights = rand(Normal(150, 2), 100)
ll_data(μ, σ) = begin
d = Normal(μ, σ)
pdf.(d, heights) .|> log |> sum
end
l_param(θ, d) = pdf(d, θ) |> log
l_priors(μ, σ) = l_param(μ, prior_mean) + l_param(σ, prior_sd)
objective_fn(μ, σ) = ll_data(μ, σ) + l_priors(μ, σ)
df(a, b) = gradient(objective_fn, a, b)[1]
d2f(a, b) = gradient(df, a, b)
# this works
println(df(153.5, 7.0))
# this fails
println(d2f(153.5, 7.0))
```

The last line throws a `Can't differentiate loopinfo expression`

exception.

What does this mean and how to resolve it?

Upgrading to Julia 1.4.0-rc1 didn’t work either.

Using ForwardDiff works (on Julia 1.3.1):

```
using ForwardDiff
f(arr) = begin
a, b = arr
objective_fn(a, b)
end
g(x) = ForwardDiff.hessian(f, x)
g([153.5, 7.0])
```