Paul Bürkner and Steve Bronder, live at StanCon 2026, discuss amortized inference, likelihood-free models, GPU computing in Stan, and sum-to-zero constraints.

Website for the Bayesian Workflow book by Gelman, Vehtari, McElreath, et al. — case studies, code, and exercises in R and Stan.

Join Prof. Frank E. Harrell Jr. (Vanderbilt University), a leading authority in applied biostatistics and regression methodology, and Drew G. Levy, PhD, for an online seminar designed for PhD students and professional researchers seeking to strengthen their quantitative research practice. Drawing on Regression Modeling Strategies (Harrell, 2015) and the continuously updated online materials, this workshop covers contemporary approaches to specifying, fitting, validating, and interpreting multivariable regression models for prediction and inference. Participants will explore key challenges in empirical research, including nonlinearity and interactions, overfitting, missing data, model validation, causal inference-based model specification, comparisons between regression and machine-learning approaches, and modeling continuous, binary, ordinal, longitudinal, and survival outcomes. With a strong emphasis on principled model formulation, graphical interpretation, reproducible workflows, and applied R examples using the rms package, the seminar equips participants to produce more robust, transparent, and publishable analyses. The seminar is available via live Zoom sessions or asynchronously with recordings and materials, includes access to an expert-moderated Q&A forum, and provides an official Instats certificate of completion. #BayesianStatistics #Biostatistics #CausalInference #ComputationalStatistics #DataScience #Epidemiology #HealthSciences #LongitudinalDataAnalysis #MachineLearning #MissingData #RegressionModelingStrategies #Statisticalmodellingandregression #Statistics #PublicHealth #R #Research #ResearchTraining #Instats

How can researchers use large language models without compromising causal rigor? Join our online seminar on Causal Inference and Large Language Models, designed for PhD students, postdoctoral researchers, and academic staff working with textual, social media, administrative, or mixed-format data. Taught by Moses Boudourides (Northwestern University), this seminar explores how LLMs can be integrated into causal inference workflows to measure latent constructs, generate candidate instruments, support causal discovery, estimate treatment effects with high-dimensional text data, and audit bias in LLM-derived variables. Participants will engage with conceptual foundations and applied workflows using reproducible, fully annotated Python and R scripts, diagnostic tools, and publication-oriented strategies. No prior knowledge of causal inference, LLMs, programming, or coding is required; general familiarity with quantitative research is sufficient. The seminar is delivered online via Zoom, with recordings, slides, scripts, data, readings, and a monitored Q&A forum available for 30 days after the seminar. Participants receive an official Instats certificate of completion, with ECTS Equivalent points where applicable. #AppliedResearch #BayesianStatistics #CausalInference #ComputationalSocialScience #ComputationalStatistics #ComputerScience #DataScience #Economics #EnvironmentalSciences #Finance #Healthcare #MachineLearning #Management #MixedMethods #OrganizationStudies #RegressionModelingStrategies #ResearchMethods #SocialSciences #Sociology #Statisticalmodellingandregression #Statistics #SocialTheory #Python #R #Research #ResearchTraining #Instats

Join our online seminar on R programming for contemporary statistical inference and machine learning, designed for PhD students and professional researchers working with high-dimensional biological, clinical, imaging, or observational data. Led by Nikolay Oskolkov, Group Leader (PI) at LIOS, this seminar combines methodological rigor with hands-on R implementation, covering reproducible workflows, linear models, LASSO, Partial Least Squares, discriminant analysis, PCA, tSNE, UMAP, clustering methods, neural networks, Random Forests, model diagnostics, feature selection, and interpretability. Participants will strengthen their ability to design, implement, and report complex analyses in publication-ready research pipelines while gaining a deeper understanding of both classical inference and modern machine learning approaches. The seminar is delivered via Zoom, with recordings, slides, code, data, and a monitored Q&A forum available for 30 days after the seminar, making it easy to attend live or asynchronously. An official Instats certificate of completion is provided, with ECTS Equivalent points available where applicable. #BayesianStatistics #Bioinformatics #Biostatistics #DataScience #MachineLearning #Scientificprogramming #Statistics #ComputerScience #SoftwareEngineering #RStudio #Research #ResearchTraining #Instats

How can researchers use large language models without compromising the rigor of causal inference? This online Instats seminar, taught by Moses Boudourides (Northwestern University), explores how LLMs can be integrated into defensible causal workflows for textual, social media, administrative, and mixed-format data. Designed for PhD students, postdoctoral researchers, and academic staff, the seminar covers how to formalise causal questions, use LLMs to measure latent constructs, address prediction error and bias, generate and evaluate candidate instruments, support causal discovery, and estimate treatment effects with high-dimensional text data. No prior knowledge of causal inference, LLMs, or programming is required; participants receive fully annotated Python and R scripts, lecture slides, diagnostic tools, a curated reading list, access to recordings and materials for 30 days, a monitored Q&A forum, and an official Instats certificate of completion. #AppliedResearch #BayesianStatistics #CausalInference #ComputationalSocialScience #ComputationalStatistics #ComputerScience #DataScience #Economics #EnvironmentalSciences #Finance #Healthcare #MachineLearning #Management #MixedMethods #OrganizationStudies #RegressionModelingStrategies #ResearchMethods #SocialSciences #Sociology #Statisticalmodellingandregression #Statistics #SocialTheory #Python #R #Research #ResearchTraining #Instats

Statistica bayesiana per le scommesse: prior, likelihood, posterior e Bayesian Updating live. Guida completa con esempi su calcio, tennis e Poisson Model.

Learn spatial phylogenetics & Bayesian phylogenetic mixed models in R. Practical training in phylogenetic modelling & comparative workflows.

Learn Bayesian regression and mixed-effects models using brms. Practical online training in hierarchical modelling and Bayesian workflows.

Bayesian Statistical Modelling with Stan and brms is an advanced R training course for researchers who want to apply Bayesian statistical methods to real research data. The course focuses on building,...

5-day live online Bayesian modelling course using R-INLA. Learn priors, hierarchical models, spatial analysis, and Bayesian inference.

Stable Isotope Mixing Models Using SIBER, SIAR, and MixSIAR is a practical R training course for ecologists and environmental researchers using stable isotope data. The course focuses on applying Baye...

A two day online course teaching Python for data science and statistical computing. Learn programming essentials, NumPy, Pandas, data visualisation, and basic statistical analysis using real datasets....

Master spatial and spatial-temporal statistical modelling techniques using R-INLA in this advanced training course from PR Statistics. Designed for researchers, data scientists, and applied statistici...

In an era of AI-generated content, persuasive but weakly supported claims, and rapidly shifting technical narratives, researchers need more than methodological skill: they need disciplined critical judgement. Join Leonid Chindelevitch from the School of Public Health, Imperial College London, for an online Instats seminar designed for PhD students, early-career academics, and professional researchers who want to strengthen their ability to evaluate evidence, identify hidden assumptions, recognise cognitive biases, apply Bayesian reasoning, stress-test arguments, and distinguish genuine expertise from performative authority. The seminar offers a practical toolkit for analysing contested claims, improving literature appraisal, refining hypotheses, strengthening grant applications and manuscripts, and using AI tools to sharpen rather than replace critical thinking. Delivered via Zoom with recordings, materials, and a monitored Q&A forum available for 30 days, the seminar also includes an official Instats certificate of completion, with ECTS Equivalent points where applicable. #ArtificialIntelligence #BayesianStatistics #CognitiveScience #Epistemology #Real-WorldEvidence #SoftSkills #TechnologyPolicy #Well-being #Psychology #DataScience #InformationScience #Statistics #PhilosophyofScience #PublicHealth #Research #ResearchTraining #Instats

Strengthen the credibility and impact of your empirical research with our online seminar on contemporary regression modeling strategies for PhD students and professional researchers. Drawing on Frank E. Harrell Jr.’s Regression Modeling Strategies and taught by Prof. Harrell, with causal inference-based model specification led by Drew G. Levy, PhD, this workshop covers principled approaches to specifying, fitting, validating, and interpreting multivariable regression models for prediction and inference. Participants will explore key challenges in quantitative research, including nonlinearity and interactions, overfitting, missing covariates, model validation, comparisons with machine learning, and modeling for continuous, binary, ordinal, longitudinal, and survival outcomes. Through focused instruction, applied case studies, and reproducible R-based examples using the rms package, attendees will learn practical strategies for spline modeling, shrinkage and penalization, bootstrap and cross-validation, causal-informed variable selection, and publication-ready visualization of complex model results. The seminar is available live via Zoom or asynchronously with recordings and materials, includes access to a monitored Q&A forum after the live sessions, and provides an official Instats certificate of completion. #BayesianStatistics #Biostatistics #CausalInference #ComputationalStatistics #DataScience #Epidemiology #HealthSciences #LongitudinalDataAnalysis #MachineLearning #MissingData #RegressionModelingStrategies #Statisticalmodellingandregression #Statistics #PublicHealth #R #Research #ResearchTraining #Instats

In an information environment increasingly shaped by AI-generated content, persuasive but weakly supported claims, and fast-moving technical narratives, researchers need more than subject expertise: they need disciplined tools for judging credibility under uncertainty. This online Instats seminar, led by Leonid Chindelevitch of the School of Public Health at Imperial College London, is designed for PhD students, postdoctoral researchers, and professional academics who want to strengthen their ability to evaluate evidence, identify hidden assumptions, recognise cognitive biases and rhetorical traps, apply Bayesian reasoning, stress-test hypotheses, and use critical thinking as a practical research instrument. The seminar is especially relevant for those working with AI, public health, biomedical research, data-rich social science, real-world evidence, technology policy, or any field where complex claims must be interpreted carefully and defended rigorously. Participants will develop habits that support stronger dissertations, sharper articles, more credible grant applications, and more resilient peer-review responses, while also exploring how AI tools can sharpen rather than replace scholarly judgement. The seminar is delivered online via Zoom, with recordings and materials available for 30 days, a monitored Q&A forum, and an official certificate of completion provided at the end. #ArtificialIntelligence #BayesianStatistics #CognitiveScience #Epistemology #Real-WorldEvidence #SoftSkills #TechnologyPolicy #Well-being #Psychology #DataScience #InformationScience #Statistics #PhilosophyofScience #Research #ResearchTraining #Instats