The Bayesian method is the natural approach to inference, yet it is hidden from readers behind chapters of slow, mathematical analysis. The typical text on Bayesian inference involves two to three ...

The choice of PyMC as the probabilistic programming language is two-fold. As of this writing, there is currently no central resource for examples and explanations in the PyMC universe. The official ...

Probabilistic programming has emerged as a powerful paradigm for constructing and analysing statistical models by combining the expressiveness of modern programming languages with the rigour of ...

Most recent advances in artificial intelligence—such as mobile apps that convert speech to text—are the result of machine learning, in which computers are turned loose on huge data sets to look for ...

The human ability to think about thinking ("theory of mind") is a fundamental object of study in many disciplines. In recent decades, researchers across these disciplines have converged on a rich ...

Probabilistic Programming is a way of defining probabilistic models by overloading the operations in standard programming language to have probabilistic meanings. The goal is to specify probabilistic ...

Researchers can demonstrate that on some standard computer-vision tasks, short programs -- less than 50 lines long -- written in a probabilistic programming language are competitive with conventional ...

Abstract: Manipulation tasks require robots to reason about cause and effect when interacting with objects. Yet, many data-driven approaches lack causal semantics and thus only consider correlations.