just checked this book out online. Its an excellent read and its model-agnostic framework is really clarifying, instead of getting lost in particulars of syntax, the book allows focus on creating insight.

{causact} 📦 v0.5.8 is on its way to CRAN. Learn computational Bayesian inference using this intuitive and visual #rstats package. Release includes a little intro vignette: cran.r-project.org/web/packages...

🧠 LLMs aren't technically Bayesian, but they're conditional probability powerhouses! They train with maximum likelihood yet output evolving token probabilities that update with each new piece of info. This visual captures how a neutral stock trend shifts up or down as new words enter the prompt.

Education has traditionally focused on teaching skills, e.g. coding in Python. But with AI making it easier to acquire/apply skills, the real differentiator isn’t skill acquisition—it’s the ability to iteratively deliver outcomes that align with the goals, values, and needs of companies or clients.

🚀 Causality refresher! 🚀

Check out this Shiny app that uses DAGs to show how including (or controlling for) a variable (z) can help or hurt your causal inference for how x -> y. Be sure to block your back door! 🎯

{causact} 📦 v0.5.7 is on its way to CRAN. {causact} 📦 helps you visualize your Bayesian modelling assumptions using #rstats and then automates lightning fast inference using #numpyro behind the scenes. Give it a try!

Nice words from Nelson were the tipping point for me. I am here on bluesky now. Please enjoy my free text below😀. Thx!