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Jonah Gabry

Staff Associate

Causal inference using hierarchical and nonparametric Bayesian interaction models

Informative priors for Bayesian inference and regularization

NYU/Columbia Quantitative Postdoctoral Training Program in the Education Sciences

Regina Dolgoarshinnykh

Associate Research Scientist in the Department of Statistics; Adjunct Assistant Professor of Statistics

Sloan Proposal for Stan

To support the development, maintenance, and dissemination of Stan, a probabilistic programming language that simplifies Bayesian modeling and data analysis.

Stan, Scalable Software for Bayesian Modeling

This award is to design, code, document, test, dissememinate, and maintain Stan, an extensible open-source software framework and compiler for efficient and scalable Bayesian statistical modeling. Stan is an extensible, open-source, cross-platform software framework for developing Bayesian statistical models. The first step in Bayesian modeling is setting up a full probability model for all quantities of interest. Stan facilitates this process by providing an expressive and extensible domain-specific programming language for specifying probabilistic models.

Latent Space Models for Aggregated Relational Data in Social Sciences

How does the understanding of social networks contribute to social science? In particular, (1) which network features or observable characteristics encode social structure; (2) how do these features contribute to the formation of connections or social ties; and (3) how does network structure impacts diffusion, specifically the spread of influences, opinions, and diseases? A key difficulty in studying these questions is that most contributions to current understanding in this area come from a small number of applications where full network data are readily available.

Tian Zheng

Professor of Statistics; Chair, Department of Statistics

Solving Difficult Bayesian Computation Problems in Education Research with Stan

Some statistical models used in education research are complex. This complexity arises in part because the data structures that underlie these statistical models involve multiple nested (i.e., cross-classified multilevel models) and non-nested groupings (i.e., partially-nested designs). Another source of complexity in these models results from the fact that key variables, such as student achievement, can be measured only indirectly and are represented in the model by latent variables.

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