James Azam

Oh really? it works for me and I don't see a paywall. Maybe try a different browser?

Visualization in Bayesian Workflow Abstract. Bayesian data analysis is about more than just computing a posterior distribution, and Bayesian visualization is about more than trace plots of M

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- Gabry, J., Simpson, D., Vehtari, A., Betancourt, M., & Gelman, A. (2019). Visualization in Bayesian Workflow. Journal of the Royal Statistical Society Series A: Statistics in Society, 182(2), 389–402. doi.org/10.1111/rssa...

Bayesian Workflow The Bayesian approach to data analysis provides a powerful way to handle uncertainty in all observations, model parameters, and model structure using probability theory. Probabilistic programming lang...

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- Gelman, A., Vehtari, A., Simpson, D., Margossian, C. C., Carpenter, B., Yao, Y., Kennedy, L., Gabry, J., Bürkner, P.-C., & Modrák, M. (2020). Bayesian Workflow (No. arXiv:2011.01808). arXiv. arxiv.org/abs/2011.01808

Toward a principled Bayesian workflow in cognitive science Experiments in research on memory, language, and in other areas of cognitive science are increasingly being analyzed using Bayesian methods. This has been facilitated by the development of probabilist...

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- Schad, D. J., Betancourt, M., & Vasishth, S. (2020). Toward a principled Bayesian workflow in cognitive science (No. arXiv:1904.12765). arXiv. arxiv.org/abs/1904.12765

Bayesian hierarchical modeling: an introduction and reassessment - Behavior Research Methods With the recent development of easy-to-use tools for Bayesian analysis, psychologists have started to embrace Bayesian hierarchical modeling. Bayesian hierarchical models provide an intuitive account ...

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- Veenman, M., Stefan, A. M., & Haaf, J. M. (2023). Bayesian hierarchical modeling: An introduction and reassessment. Behavior Research Methods. doi.org/10.3758/s134...

Moving beyond noninformative priors: why and how to choose weakly informative priors in Bayesian analyses Throughout the last two decades, Bayesian statistical methods have proliferated throughout ecology and evolution. Numerous previous references established both philosophical and computational guideli...

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- Lemoine, N. P. (2019). Moving beyond noninformative priors: Why and how to choose weakly informative priors in Bayesian analyses. Oikos, 128(7), 912–928. doi.org/10.1111/oik....

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- Säilynoja, T., Johnson, A. R., Martin, O. A., & Vehtari, A. (2025). Recommendations for visual predictive checks in Bayesian workflow (No. arXiv:2503.01509). arXiv. doi.org/10.48550/arX...

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- Nicenboim, B., & Vasishth, S. (2016). Statistical methods for linguistic research: Foundational Ideas—Part II. Language and Linguistics Compass, 10(11), 591–613.

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- Goligher, E. C., Heath, A., & Harhay, M. O. (2024). Bayesian statistics for clinical research. The Lancet, 404(10457), 1067–1076. doi.org/10.1016/S014...

Bayesian statistics and modelling - Nature Reviews Methods Primers This Primer on Bayesian statistics summarizes the most important aspects of determining prior distributions, likelihood functions and posterior distributions, in addition to discussing different appli...

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- van de Schoot, R., Depaoli, S., King, R., Kramer, B., Märtens, K., Tadesse, M. G., Vannucci, M., Gelman, A., Veen, D., Willemsen, J., & Yau, C. (2021). Bayesian statistics and modelling. Nature Reviews Methods Primers, 1(1), 1–26. doi.org/10.1038/s435...

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- Vasishth, S., Nicenboim, B., Beckman, M. E., Li, F., & Kong, E. J. (2018). Bayesian data analysis in the phonetic sciences: A tutorial introduction. Journal of Phonetics, 71, 147–161. doi.org/10.1016/j.wo...

How to become a Bayesian in eight easy steps: An annotated reading list - Psychonomic Bulletin & Review In this guide, we present a reading list to serve as a concise introduction to Bayesian data analysis. The introduction is geared toward reviewers, editors, and interested researchers who are new to B...

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- Etz, A., Gronau, Q. F., Dablander, F., Edelsbrunner, P. A., & Baribault, B. (2018). How to become a Bayesian in eight easy steps: An annotated reading list. Psychonomic Bulletin & Review, 25(1), 219–234. doi.org/10.3758/s134...

Bayesian data analysis for newcomers - Psychonomic Bulletin & Review This article explains the foundational concepts of Bayesian data analysis using virtually no mathematical notation. Bayesian ideas already match your intuitions from everyday reasoning and from tradit...

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- Kruschke, J. K., & Liddell, T. M. (2018). Bayesian data analysis for newcomers. Psychonomic Bulletin & Review, 25(1), 155–177. doi.org/10.3758/s134...

Introduction to the Concept of Likelihood and Its Applications - Alexander Etz, 2018 This Tutorial explains the statistical concept known as likelihood and discusses how it underlies common frequentist and Bayesian statistical methods. The artic...

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- Etz, A. (2018). Introduction to the Concept of Likelihood and Its Applications. Advances in Methods and Practices in Psychological Science, 1(1), 60–69. doi.org/10.1177/2515...
doi.org/10.1111/lnc3...

Bayesian Data Analysis Primers/Tutorials

I gathered these primers for learning bayesian data analysis, mainly for myself but I hope they are helpful to you too.

If you know of similar articles, do share them in the comments.

#bayesiananalysis #datascience #machinelearning #rstats #python

Changelog

#epinowcast 0.4.0 is out! This release has been a long time coming and contains work from many contributors. It includdes new features, better better and clearer documentation.

package.epinowcast.org/news/index.h...

ggplot2 4.0.0 A new major version of ggplot2 has been released on CRAN. Find out what is new here.

I am beyond excited to announce that ggplot2 4.0.0 has just landed on CRAN.

It's not every day we have a new major #ggplot2 release but it is a fitting 18 year birthday present for the package.

Get an overview of the release in this blog post and be on the lookout for more in-depth posts #rstats

It covers everything from introductions to methods, results, tables/figures, discussions, journal choice, and even how to respond to reviewers.

If you’re writing, teaching, or mentoring, this is gold.

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Its strengths are:

- Clear & simple
- 1-page papers with checklists
- Useful for any field
- Perfect for teaching & refreshing

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My go-to resource for scientific writing

The Writing Tips Series by the Journal of Clinical Epidemiology is hands down one of the best guides for academic writing: www.sciencedirect.com/special-issu... (1/3)

#AcademicWriting #Research #PhDLife #ScientificWriting #Masters #PhD #AcademicChatter

@seabbs.bsky.social Isn't this the dream for all our users?

A developer's love language.

I could curate that for you.

5. Nature Methods – Statistics for Biologists / Points of Significance
www.nature.com/collections/...

Great primers on error bars, power, regression, correlation vs causation, and visualisation.

Epidemiology 2005 Appropriate sample sizing, selection of controls in case-control studies, and clear end-points and interpretation of subgroup analyses are crucial for robust epidemiological research.

4. The Lancet’s Epidemiology Series 2005
www.thelancet.com/series-do/ep...

Goes deeper into sample size, controls, likelihood ratios, and multiplicity in trials.

Epidemiology 2002 Increasing demands on the time of clinicians today reduce their opportunities to stay abreast of medical literature, and many report that they cannot read the literature critically. Information on res...

3. The Lancet’s Epidemiology Series 2002
www.thelancet.com/series-do/ep...

Introduces study designs, bias, cohort & case-control studies, randomisation, blinding, and screening.

2. Statistics Notes – BMJOpen
www.bmj.com/specialties/...

Short explainers on P values from confidence intervals, bootstrap methods, odds ratios, sampling error, and more.

1. Statistics at Square One – BMJOpen
www.bmj.com/about-bmj/re...

Covers the basics: data display, confidence intervals, t-tests, chi-squared tests, regression, survival analysis, and study design.

These are great for teaching, refreshing your memory, or sharing with colleagues new to statistical and epidemiological methods.

Do you know of other similar resources worth adding to this list?

I’ve found these really useful resources for clear, accessible explanations of core statistics and epidemiology methods and concepts. These are great for teaching, refreshing your memory, or sharing with colleagues new to statistical and epidemiological methods.