Making MCSE for constant arrays consistent

[sethaxen]

Currently mcse behaves inconsistently for constant arrays depending on the estimator and method:

julia> using MCMCDiagnosticTools, Statistics

julia> x = ones(100);

julia> mcse(x; kind=mean)
NaN

julia> mcse(x; kind=std)
NaN

julia> mcse(x; kind=median) # hangs because ESS is NaN, which prevents `betainvcdf` from terminating
^CERROR: InterruptException:
...

julia> mcse(x; kind=x -> mean(x))  # using sbm
0.0

julia> mcse(x; kind=x -> std(x))  # using sbm
0.0

julia> mcse(x; kind=x -> median(x))  # using sbm
0.0

We could either:

• return 0.0 for constant arrays
• return NaN for constant arrays

I feel that returning NaN makes more sense. Constant arrays are most likely to occur either when a sampled parameter has some fixed entries (e.g. the diagonal of a correlation matrix) or when all transitions that perturbed the parameter were rejected; this often indicates failed sampling. Since MCSE is primarily useful as a convergence diagnostic, I think returning NaN makes the most sense here.