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Feature request: permit location of 0/negative_infinity() in neg_binomial_2(_log)_lpmf #3302
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I think that would be OK. May I ask why you care about these boundary cases? Do they show up in real-world models you're fitting?
Also, we need to work out what the gradients are to avoid blow up if the 0 is an autodiff variable. The negative infinity case is harder for gradients.
In occupancy models where sites
$i \in 1:I$ are surveyed over surveys$j \in 1:J$ , I use the proportion of the maximum survey length$\Delta$ as offsets for each survey. Sometimes, sites "fail" to survey when for example a camera trap was malfunctioning. So in my models, I have to loop over every survey$j$ for each site$i$ to check if the offset was 0:for (i in 1:I) { for (j in 1:J) { if (!is_inf(log_Delta[j, i])) { target += ... } } }
Ideally, I could just vectorise over all surveys
$j$ and handle the0/negative_infinity()inside the_lpmffunctions. Note that when$\Delta_{ij} = 0$ , the linear predictor is$-\infty$ .Thanks---that's a really useful example.
An alternative way to code that now would be to use long form rather than wide form for
(i, j). It will be more efficient if there are a lot of zeros, otherwise finagling the likelihood to ignore it will be more efficient. It does require the observations to also be zero or it'll just blow up and reject.
Good day,
It is currently possible to have location parameter of 0 in
poisson_lpmfandnegative_infinity()inpoisson_log_lpmf; I've also verified this works with the_glmfunctions:However, this is not possible with
neg_binomial_2functions:which errors out with:
Exception: neg_binomial_2_lpmf: Location parameter is 0, but must be positive finite!I think it would be reasonable to permit similar behaviour as for the Poisson functions?