My research interests are Bayesian probability theory and methodology, Bayesian workflow, probabilistic programming, inference methods such as Laplace, EP, VB, MC, inference and model diagnostics, model assessment and selection, Gaussian processes, and hierarchical models. My 15min introduction to my research on computational probabilistic modeling
Gelman, Jennifer Hill, and Aki Vehtari (2020). Regression and other stories. Cambridge University Press. Publisher’s webpage for the book. Home page for the book. Back cover text: Many textbooks on regression focus on theory and the simplest of examples. Real statistical problems, however, are complex and subtle. This is not a book about the theory of regression. It is a book about how to use regression to solve real problems of comparison, estimation, prediction, and causal inference. It focuses on practical issues such as sample size and missing data and a wide range of goals and techniques. It jumps right in to methods and computer code you can use fresh out of the box.
Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari and Donald B. Rubin (2013). Bayesian Data Analysis, Third Edition. Chapman and Hall/CRC. Winner of the 2016 De Groot Prize from the International Society for Bayesian Analysis (awarded to the author or authors of an outstanding published book in Statistical Science). Publisher’s webpage for the book. Home page for the book. Errata for 3rd edition. Electronic edition for non-commercial purposes only. Online course material, including video lectures, slides, code, and notes for most of the chapters.
A member of the Stan development team. Stan is a probabilistic programming language and framework implementing full Bayesian statistical inference.
A member of development team of ArviZ
- Exploratory analysis of Bayesian models
Co-author of GPstuff - Gaussian process models for Bayesian analysis.