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 ...

Machine learning (ML) and probabilistic programming are two powerful tools in the arsenal of data science, each with its own strengths. ML excels at making predictions from complex data, while ...

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 ...

Probabilistic programming is a powerful technique that allows you to create and manipulate models of uncertainty and learning from data. It can be applied to many domains, but one of the most ...

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 ...

Inferring reliable brain-behavior associations requires synthesizing evidence from thousands of functional neuroimaging studies through meta-analysis. However, existing meta-analysis tools are limited ...