This tutorial will introduce a new paradigm for agent-based models (ABMs) that leverages automatic differentiation (AD) to efficiently compute simulator gradients. In particular, this tutorial will ...

Practical Probabilistic Programming introduces the working programmer to probabilistic programming. In it, you'll learn how to use the PP paradigm to model application domains and then express those ...

Bayesian statistical methods provide powerful tools for solving various data science problems. The Bayesian approach yields easy-to-interpret results and automatically accounts for uncertainty in our ...

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 ...

Abstract: Probabilistic logic programming extends logic programming by enabling the representation of uncertain information by means of probability theory. Probabilistic logic programming is at the ...