מידול סטטיסטי בשיטה הבייסיאנית ב־R 

Bayesian Statistics: So Much More Than Just the Bayes factor

With: Mattan S. Ben-Shachar, PhD (about me)

Traditional statistical methods often leave researchers wrestling with ambiguity, struggling to translate limited information into insights and conclusions. Enter Bayesian statistics: a statistical framework that lets you speak the language of probabilities, make decisions even with limited data, and confidently navigate uncertainty.

Join me on a 2-day (12 hour) research-focused workshop, where together we’ll delve into the practical and theoretical foundations of Bayesian methods, using the powerful {brms} package in R.

You’ll learn to:

• Formalize your research hypotheses as prior distributions.

• Integrate your data seamlessly, updating your priors and generating evidence-based posteriors.

• Move beyond null hypothesis testing and embrace uncertainty quantification, revealing the full spectrum of possibilities your data supports.

• Tackle complex research challenges with flexible models.

Graduate students and faculty: join us to unlock the transformative power of Bayesian statistics. Enhance your research toolbox, craft compelling arguments, and publish impactful findings – all while embracing the nuanced language of probabilities.

Prerequisites: Familiarity with R - model fitting (lm, glm, (g) lmer), data tidying with the {tidyverse} building figures with {ggplot2}.

I encourage you to BYOD: Bring Your Own Data - put theory into practice.

Recommended Reading

[book] Gelman et al. (2021). Bayesian Data Analysis (3rd ed). http://www.stat.columbia.edu/~gelman/book/

[book] Kruschke, J. (2014). Doing Bayesian data analysis: A tutorial with R, JAGS, and Stan. https://sites.google.com/site/doingbayesiandataanalysis/

Code translation to brms and the tidyverse https://bookdown.org/content/3686

Makowski et al. (2019). Indices of effect existence and significance in the Bayesian framework. https://doi.org/10.3389/fpsyg.2019.02767

Rouder et al. (2018). Bayesian inference for psychology, part IV: Parameter estimation and Bayes factors. https://doi.org/10.3758/s13423-017-1420-7

van de Schoot, et al. (2021). Bayesian statistics and modelling. https://doi.org/10.1038/s43586-021-00017-2

Wagenmakers et al. (2018). Bayesian inference for psychology. Part I: Theoretical advantages and practical ramifications. https://doi.org/10.3758/s13423-017-1343-3