# Basser Seminar Series

# Stan: A probabilistic programming language for Bayesian inference

Speaker: **Dr Bob Carpenter**

Columbia University, Department of Statistics

When: Monday 30 March 2015, 2-3pm, ***Note different day and time to usual.**

Where: The University of Sydney, School of IT Building, SIT Lecture Theatre (Room 123), Level 1

Add seminar to my diary

## Abstract

I'll describe Stan's probabilistic programming language, and how it's used, including:

- examples of Stan progams
- blocks for data, parameter, and predictive quantities
- transforms of constrained parameters to unconstrained spaces, with automatic Jacobian corrections
- automatic computation of first- and higher-order derivatives
- operator, function, and linear algebra library
- vectorized density functions, cumulative distributions, and random number generators
- user-defined functions
- ordinary differential equation solvers

I'll also provide an overview of the underlying algorithms for sampling and optimization:

- adaptive Hamiltonian Monte Carlo for MCMC
- L-BFGS optimization and transforms for MLE

I'll also briefly describe the user-facing interfaces:

- RStan (R), PyStan (Python), CmdStan (command line), Stan.jl (Julia), MatlabStan (MATLAB)

I'll finish with an overview of the what's next:

- data streaming variational inference
- data parallel expectation propagation
- marginal maximum likelihood for empirical Bayes
- stiff ODE solvers

## Speaker's biography

Bob has worked as a professor of computational linguistics (Carnegie

Mellon), an industrial researcher and programmer (Bell Labs, SpeechWorks, LingPipe), and is now back in academia working in statistics (Columbia Univesity).