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…

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…

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…

IDS.190 – Topics in Bayesian Modeling and Computation Speaker: Daniel Simpson (University of Toronto) Abstract: 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…

IDS.190 – Topics in Bayesian Modeling and Computation **PLEASE NOTE ROOM CHANGE TO BUILDING 37-212 FOR THE WEEKS OF 10/30 AND 11/6** Speaker:   Jonathan Huggins (Boston University) Abstract: Standard Bayesian inference is known to be sensitive to misspecification between the model and the data-generating mechanism, leading to unreliable uncertainty quantification and poor predictive performance.…