IDS.190 - Topics in Bayesian Modeling and Computation Speaker: Brian Patton (Google AI) Abstract: TensorFlow Probability provides a toolkit to enable researchers and practitioners to integrate uncertainty with gradient-based deep learning on modern accelerators. In this talk we'll walk through some practical problems addressed using TFP; discuss the high-level interfaces, goals, and principles of the library; and touch on some recent innovations in describing probabilistic graphical models. Time-permitting, we may touch on a couple areas of research interest for the…

IDS.190 Topics in Bayesian Modeling and Computation *Note:  The speaker this week will give two shorter talks within the usual session Title:   Behavior of the Gibbs sampler in the imbalanced case Abstract:   Many modern applications collect highly imbalanced categorical data, with some categories relatively rare. Bayesian hierarchical models combat data sparsity by borrowing information, while also quantifying uncertainty. However, posterior computation presents a fundamental barrier to routine use; a single class of algorithms does not work well in all settings and…

IDS.190 – Topics in Bayesian Modeling and Computation Abstract: Probabilistic programming is an emerging field at the intersection of programming languages, probability theory, and artificial intelligence. This talk will show how to use recently developed probabilistic programming languages to build systems for robust 3D computer vision, without requiring any labeled training data; for automatic modeling of complex real-world time series; and for machine-assisted analysis of experimental data that is too small and/or messy for standard approaches from machine learning and…

IDS.190 – Topics in Bayesian Modeling and Computation Abstract: MCMC methods yield approximations that converge to quantities of interest in the limit of the number of iterations. This iterative asymptotic justification is not ideal: it stands at odds with current trends in computing hardware. Namely, it would often be computationally preferable to run many short chains in parallel, but such an approach is flawed because of the so-called "burn-in" bias.  This talk will first describe that issue and some known…

IDS.190 – Topics in Bayesian Modeling and Computation Speaker: Daniel Simpson (University of Toronto) Abstract: As data becomes more complex and computational modelling becomes more powerful, we rapidly find ourselves beyond the scope of traditional statistical theory. As we venture beyond the traditional thunderdome, we need to think about how to cope with this additional complexity in our model building.  In this talk, I will talk about a few techniques that are useful when specifying prior distributions and building Bayesian models…