I gifted myself a #Probula Friday.

The library has gained an ability to perform #posterior #inference via grid approximation - on top of the already existing importance sampling.

Had some major fun on the DSL, forcing the type system to track models for which grid approximation is allowed (only models for which we can compute posterior density in given point).

I bundled a sub-language for ranges of doubles: `50 doubles (0.0 -> 1.0)`

https://wasowski.codeberg.page/probula

#ProbabilisticProgramming

#statstab #496 Posterior predictive checks {performance}

Thoughts: Idk why more frequentist don't use ppc for their models. I can diagnose so many issues visually this way.

#error #posterior #ppc #modelfit #diagnostics #model #r #rstats #easystats

https://easystats.github.io/performance/reference/check_predictions.html

Posterior predictive checks — check_predictions

Posterior predictive checks mean "simulating replicated data under the fitted model and then comparing these to the observed data" (Gelman and Hill, 2007, p. 158). Posterior predictive checks can be used to "look for systematic discrepancies between real and simulated data" (Gelman et al. 2014, p. 169). performance provides posterior predictive check methods for a variety of frequentist models (e.g., lm, merMod, glmmTMB, ...). For Bayesian models, the model is passed to bayesplot::pp_check(). If check_predictions() doesn't work as expected, try setting verbose = TRUE to get hints about possible problems.

#statstab #466 Bayesian workflow: Prior determination, predictive checks and sensitivity analyses

Thoughts: Having a good bayesian work flow can be challenging with complex models.

#priors #bayesian #sensitivityanalysis #posterior #ppc #brms

https://pablobernabeu.github.io/2022/bayesian-workflow-prior-determination-predictive-checks-and-sensitivity-analyses/

Bayesian workflow: Prior determination, predictive checks and sensitivity analyses | Pablo Bernabeu

This post presents a run-through of a Bayesian workflow in R. The content is closely based on Bernabeu (2022), which was in turn based on lots of other references, also cited here.

Pablo Bernabeu

#statstab #464 Plotting p-check interaction {brms}

Thoughts: Annoyingly #brms doesn't natively allow plotting for interactions (that I know of). The forum has a solution.

#ppc #posterior #bayesian #modelfit #diagnostic #rstats #r #stan

https://discourse.mc-stan.org/t/plotting-pp-check-interactions/31936

Plotting pp_check interactions

I am trying to model ordinal data with an interaction of two predictors. However, I have issues with plotting interactions and individual data with the BRMS functions. My model is of the type rating ~ category*distortion+ (1|id) I can use this type of code: pp_check(mcatxdistRDpCexp, type = “bars_grouped”, group = “category”, , ndraws = 500, prob = 0.95) pp_check(mcatxdistRDpCexp, type = “bars_grouped”, group = “distortion”, , ndraws = 500, prob = 0.95) which returns these two plots: ...

The Stan Forums
La fruto del segundo trimestre de TSMC se eleva en un 60% en la parte posterior de la demanda sólida de chips de IA – ButterWord

The world’s largest contract chip manufacturer TSMC, reported better-than-expected results as AI chip demand has been boosting the company's sales.

ButterWord
Posterior a este cruce de palabras, según el creador, la joven mezcló una serie de temas, entre ellos, reproches sobre gentrificación, la guerra de clases y también señalando que los negocios de Luisito son muy caros. La discusión f #gentrificación #” #méxico #mexicano #posterior

'How good is your Laplace approximation of the Bayesian posterior? Finite-sample computable error bounds for a variety of useful divergences', by Mikołaj J. Kasprzak, Ryan Giordano, Tamara Broderick.

http://jmlr.org/papers/v26/24-0619.html

#bayesian #posterior #laplace