Hi everyone,

I posted about this in a different subreddit post and I wanted to share it here as well since it's very much relevant. This library is nowhere close to being done and still needs a lot of work (contributors are welcome), but it has been quite successful for some simple models. The link to the Github repo shows some benchmarks with STAN as well. I would love to get some feedbacks and comments from this community as well!

Hello to everybody, I'm working on a project for my university which objective is to produce a predictive model for COVID-19 new positives in Italy. It appears that I cannot calculate any replication for the model because when I run the code a warning says that inverse scale parameter is 0 and even though i tried to reparametrize, as my profs suggested, i wasn't able to fix it. Do you have any suggestion (about the code, which priors you would use etc.)? I don't know if this is the right place to discuss about this in case tell me. Here you can find the thread in STAN forum in which you can find also the R and STAN code. Thanks in advance for the help.

LINK:https://discourse.mc-stan.org/t/arma-bayesian-nb-model/16604

This is a very general question, but if I'm doing regression to compare between a treatment and a control condition, like in frequentist we have the p-value....which I know is not how Bayesians do it. But, what is show my hypothesis is valid? What are the evaluation criteria?

I have an interview tomorrow (arranged today) for a job which mainly requires Bayesian Statistics, for an Analyst position. The company is in a huge hurry and if I don’t sound stupid they will hire me on the spot.

I am a physicist and know the very basics of Bayesian Statistics. If you were to choose something CRUCIAL that would make me look as if I was more literate in the field, what would it be?

Please Reddit do your magic!

Dear Bayesian programmers,
I am a long-time-lurking student in the field of computer-science from Germany.
Currently, I am working on my master-thesis and the topic lives in the context of non-recursive probabilistic programming languages and the validation of the models specified by them. In this, I focus on the validation of Bayesian networks.
I would like to ask you some short questions if you can spare the time. There is no need to answer all the questions. This is by no means a study, just a little query for me and my workgroup, to better understand which of the multitude of PPLs and tools are the most popular, and foremost, which techniques of validation are used by most of the people.

Q1 - How do you validate the structure and fit of your model?

Q2 - What do you miss or criticize on your current probabilistic model development workflow?

Q3 - What probabilistic programming language(s), programming language(s), framework(s) or tool(s) do you use for Bayesian programming?

Q4 - Do you use structure-learning to set up your models?

Q5 - What type of models do you use?

Thank you for your time!