trap improper posteriors
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feature
- Dominant language
- C++
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Description
We want to be able to trap improper posteriors. Daniel just demoed this model
data {
int<lower=0> N;
vector[N] x;
vector[N] y;
}
parameters {
real a;
real b;
real<lower=0> sigma;
}
model {
y ~ normal(a + b * x, sigma);
}
and it samples and gives garbage (low n_eff for a and b, but high n_eff for err_sd).
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
No source files, tests, or entry points are named. Start by reproducing the supplied model and reviewing how Stan currently handles sampling and diagnostics for improper posteriors. The issue is complete when the intended improper-posterior condition is detected and reported with a corresponding regression test.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- cpp
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100