Statistical Rethinking 2023 - Geocentric Models
Explore geocentric models in statistical thinking, covering Gaussian distributions, workflows, generative models, and analysis techniques for improved statistical reasoning.
Statistical Rethinking 2023 - The Garden of Forking Data
Explore Bayesian statistics through the "Garden of Forking Data" metaphor. Learn about generative models, probability, testing, and posterior distributions for improved statistical reasoning and prediction.
Statistical Rethinking 2022 - Generalized Linear Madness
Explore advanced statistical concepts like geometric models, state-based analysis, and population dynamics through innovative examples and practical applications.
Statistical Rethinking 2022 - Missing Data
Explore Bayesian imputation techniques for handling missing data, including DAG analysis, conceptual understanding, and practical implementation using Stan.
Statistical Rethinking 2022 - Measurement Error
Explore measurement error in statistical analysis, covering topics like income recall, divorce rates, misclassification, and floating point issues. Gain insights into modeling and addressing data inaccuracies.
Statistical Rethinking 2022 - Correlated Varying Effects
Explore advanced statistical concepts including varying effects, correlations, and non-centered coding. Learn to analyze complex data structures and improve model interpretability in this comprehensive lecture.
Statistical Rethinking 2022 - Multi-Multilevel Models
Explore advanced statistical concepts including multiple cluster types, multilevel predictions, and non-centered priors in this comprehensive lecture on multi-multilevel models.
Statistical Rethinking 2022 - Multilevel Models
Explore multilevel models through coffee golems, tadpoles, and predators. Learn regularization techniques and statistical concepts for advanced data analysis.
Statistical Rethinking 2022 - Ordered Categories
Explore advanced statistical concepts like ordered categories, cumulative log-odds, and Dirichlet priors. Learn to handle sample bias, confounding, and complex causal effects in statistical analysis.
Statistical Rethinking 2022 - Modeling Events
Explore statistical modeling of events, covering generative models, generalized linear models, logistic regression, and binomial regression. Learn to analyze gender applications and calculate marginal causal effects.
Statistical Rethinking 2022 - Counts & Confounds
Explore statistical rethinking with focus on counts and confounds. Learn about confounded admissions, sensitivity analysis, proxies, factors, and Poisson GLMs in this comprehensive lecture.
Statistical Rethinking - Markov Chain Monte Carlo
Explore Markov chain Monte Carlo and Hamiltonian Monte Carlo methods, including practical implementation in Stan and diagnostic techniques for statistical modeling.
Statistical Rethinking 2022 - Good & Bad Controls
Explore causal inference through DAGs, backdoor criterion, and control variables. Learn to identify good and bad controls for accurate statistical analysis and avoid common pitfalls.
Statistical Rethinking 2022 - Elemental Confounds
Explore key concepts in statistical analysis: forks, pipes, colliders, and unobserved confounds. Learn to identify and address elemental confounds for more robust research designs.
Statistical Rethinking 2022 - Categories, Curves & Splines
Explore statistical concepts like categorical variables, contrasts, and splines. Learn Bayesian causal inference and polynomial models for advanced data analysis and modeling techniques.
Statistical Rethinking 2022 - Geocentric Models
Explore Bayesian linear regression, covering model language, generative and statistical approaches, validation, and posterior predictions for a comprehensive understanding of statistical modeling.
Statistical Rethinking 2022 - Bayesian Inference
Explore Bayesian inference through practical examples like globe tossing. Learn updating, sampling, and computing distributions using grid approximation and posterior predictive methods.
Statistical Rethinking 2022 - Golem of Prague
Explore statistical modeling, scientific workflow, and causal inference through the lens of golems, owls, and DAGs in this thought-provoking introduction to statistical rethinking.
Science Before Statistics - Causal Inference
Comprehensive workshop on causal inference, covering key concepts like causal design, graph analysis, and Bayesian inference. Equips researchers with tools to navigate complex causal relationships in data analysis.
Bayesian Inference Is Just Counting
Explore foundations of Bayesian data analysis, focusing on conceptual understanding and causal inference. Learn key principles from renowned expert Richard McElreath's book, covering topics like T-tests and machine learning.
Bayesian Statistics Without Frequentist Language
Explore Bayesian statistics from a fresh perspective, focusing on its core concepts and applications without relying on traditional frequentist terminology.