We develop new methods and algorithms for coping with uncertainty in artificial intelligence, focusing in particular on approximate Bayesian inference of probabilistic programs. We also solve ...

We study brains and computers alike as statistical inference engines which are probabilistic, approximate, active, robust, and resource-constrained. We develop new methods for approximate Bayesian ...

Bayesianism is enjoying a revival across many fields, and it offers a powerful tool for improving inference and analytic transparency in qualitative research. This course introduces basic principles ...

Integrated Inferences develops a framework for using causal models and Bayesian updating for qualitative and mixed-methods research. By making, updating, and querying causal models, researchers are ...