Eero Linna

I probably explained 1. a bit poorly. What I mean is that - We know that 10 000 draws doesn't give an accurate posterior mean (or some other expectation) for...

This is what I'm essentially hearing: 10 000 samples will have a small error compared to 100 000 and thus it's fine to use the smaller sample to compute log_lik...

Would this be an additional slot in the model info file like `blr.info.json` or in a model code file like `blr.stan`?

So you're thinking that `blr.info.json` would be something like this? ```json { "name": "blr", "keywords": [], "title": "A Bayesian linear regression model with vague priors", "description": "A Bayesian linear regression...

With PyMC there is no need for any extra code to compute predictive distributions ```python posterior_predictive_samples = pymc.sample_posterior_predictive(draws, model=model) ``` where model is any pymc model and draws are the...

How should we expose the likelihood/predictive distribution to users? In other words, if I have a posterior object `po

That's a good idea!

Do we want to have an API that exposes the likelihood code? Something like ```R stan_likelihood_code_file(po) ``` that would for the blr posterior return `models/stan/blr_likelihood.stan`. Or is it sufficient that...

Is this issue essentially just a reminder? So you already know what needs to be done to fix this and don't need any input from others. Or is it that...

Ah yeah so essentially in this case the parameters for the distribution are given directly and the stan model only draws samples from that distribution rather than estimating a posterior...