#statstab #537 {hdbayes} An R Package for Bayesian Analysis of Generalized Linear Models Using Historical Data
Thoughts: An interesting approach to priors. I'm not v familiar w this so curious what others think.
#statstab #537 {hdbayes} An R Package for Bayesian Analysis of Generalized Linear Models Using Historical Data
Thoughts: An interesting approach to priors. I'm not v familiar w this so curious what others think.
#statstab #534 Model-averaged Bayesian t tests
Thoughts: I find this testing ensemble approach very amenable to theory building. Check a few models that include/exclude assumptions you may/not care about.
#robust #ttest #bayes #bma #jasp
https://link.springer.com/article/10.3758/s13423-024-02590-5

One of the most common statistical analyses in experimental psychology concerns the comparison of two means using the frequentist t test. However, frequentist t tests do not quantify evidence and require various assumption tests. Recently, popularized Bayesian t tests do quantify evidence, but these were developed for scenarios where the two populations are assumed to have the same variance. As an alternative to both methods, we outline a comprehensive t test framework based on Bayesian model averaging. This new t test framework simultaneously takes into account models that assume equal and unequal variances, and models that use t-likelihoods to improve robustness to outliers. The resulting inference is based on a weighted average across the entire model ensemble, with higher weights assigned to models that predicted the observed data well. This new t test framework provides an integrated approach to assumption checks and inference by applying a series of pertinent models to the data simultaneously rather than sequentially. The integrated Bayesian model-averaged t tests achieve robustness without having to commit to a single model following a series of assumption checks. To facilitate practical applications, we provide user-friendly implementations in JASP and via the $$\texttt {RoBTT}$$ RoBTT package in $$\texttt {R}$$ R . A tutorial video is available at https://www.youtube.com/watch?v=EcuzGTIcorQ
If you're unfamiliar with or want to delve in deeper into Bayesian statistics, this online text is perfect for you.
Bayesian statistics should be the orthodox. Unfortunately, the super simplicity, despite erroneous answers (Jaynes, 2003), is what is commonly called Frequentist statistics is orthodox. We could call it school statistics.
The Jaynes book is Probability Theory: The Logic of Science. It's a bookshelf requirement.
Hehehehe, we got another reviewer confused by our use of a 89% credible interval.
Cue the beauty of prime numbers! And it is my co-author's birth year, I am so happy that I can put this in the answer 😅!

Nathalie Baye est morte à 77 ans à Paris, a annoncé samedi sa famille, dont sa fille Laura Smet. Multi-césarisée, la comédienne française, qui avait tourné avec de grands réalisateurs, souffrait depuis plusieurs mois de la maladie à corps de Lewy.
Monument du #cinéma #français, la #comédienne #NathalieBaye est morte
#RIP Nathalie #Bayes
Nous nous souviendrons de toi, de ta gentillesse, et de ton humanisme
https://www.france24.com/fr/france/20260418-actrice-nathalie-baye-est-morte-cannes-laura-smet-cinema

Nathalie Baye est morte à 77 ans à Paris, a annoncé samedi sa famille, dont sa fille Laura Smet. Multi-césarisée, la comédienne française, qui avait tourné avec de grands réalisateurs, souffrait depuis plusieurs mois de la maladie à corps de Lewy.
#statstab #522 Bayes Rules! Different priors, different posteriors
Thoughts: Nice illustration of how uninformative and informative priors change your posterior.
#statstab #520 Reverse‐Bayes methods for evidence assessment and research synthesis
Thoughts: I was reminded of this paper on assessing the evidentiary value of a finding. What do ppl think?
#bayes #inference #evidence #probability #priors #sensitivity