Advancing probabilistic programming for scientific applications?
#EuroSciPy2025 welcomes original research on Bayesian methods, MCMC algorithms, and statistical modeling in #Python.
Submit your work as tutorials, talks, or posters!
#BayesianStatistics #ScientificPython #PyMC #PyStan #EuroSciPy
Developing Bayesian inference methods for complex scientific problems?
#EuroSciPy2025 is seeking original work on Hamiltonian Monte Carlo, variational inference, and statistical modeling in #Python.
Submit your innovations: https://pretalx.com/euroscipy-2025/cfp #CfP
#BayesianStatistics #ScientificPython #BayesianInference #PyMC #PyStan #EuroSciPy
Ran my first PyStan model tonight. It seems a bit less documented than RStan, but the concepts are familiar enough, and the heavier lifting will be in Stan anyway.
You've heard about Stan and want to learn some more? Maybe you're about to step into the Bayesian paradigm and don't know where to start 🤔
In this week's blog post, we'll take a look what you can do with #Stan and draw comparisons to #JAGS!
A little bit about you Let’s assume you’re familiar with Bayesian statistics; you know what I mean when I say prior, likelihood and posterior. Recall that an MCMC scheme constructs a Markov chain as a method to sample from the posterior density. You may have used a probabilistic programming language (PPL) in the past, such as BUGS, to perform Bayesian inference. You’ve heard about Stan and want to learn a little more.
EuroSciPy
Ross A. Baker
Jumping Rivers