#mcmcstan — Public Fediverse posts

Sampling from a t distribution is harder than I thought. I wrote some code that implements a relatively new method by Shaw, Luu, and Brickman:
https://github.com/szego/truncated-student-t-rng

#stats #mcmcstan #mcmc

New blog post! Here's a guide to calculating the differences between categorical proportions in a principled, #bayesian way with #rstats, #mcmcstan, and {brms}, including fancy things like mosaic plots (with {ggmosaic} and striped fills (with {ggpattern}) https://www.andrewheiss.com/blog/2023/05/15/fancy-bayes-diffs-props/ #statsodon

Here's another species that provides an interesting contrast:
American Kestrel, apparently cyclical pattern in time that is largely consistent across the species' range.
#AmericanKestrel
#NABBS
#CitizenScience
#BiologicalMonitoring #mcmcStan #birds

Anyone have a good way of taking cmdstanr optimization results, reshaping them into the list form required by `init`, and then starting sampling/optimization from that point?

#mcmcstan #bayes @mcmc_stan

🚨 New #JuliaLang package! StanLogDensityProblems.jl is a really basic package that implements the LogDensityProblems.jl interface for @mcmc_stan models, built on BridgeStan.jl. It also integrates with PosteriorDB.jl, which makes it really easy to benchmark a new inference method against a large number of models. #ProbProg #MCMCStan

https://github.com/sethaxen/StanLogDensityProblems.jl

There's a podcast interviewing the O.G. Bob Carpenter, give it a listen if you're a Bayes type. Real excited to listen when I get a moment!

https://learnbayesstats.com/episode/76-past-present-future-of-stan-bob-carpenter/

#mcmcstan

What's the best way to get the Hessian from #Stan models after optimizing?

#mcmcstan

Oh, and if you want a quick and easy tldr version of that zero-inflated model post, here's a short reproducible example that you can make for yourself #rstats #bayesian @rstats #brms #MCMCstan https://gist.github.com/andrewheiss/95a139bfed0c884288c751cff3b9adb6