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 ...

Probability underpins AI, cryptography and statistics. However, as the philosopher Bertrand Russell said, “Probability is the most important concept in modern science, especially as nobody has the ...

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 ...

Understanding the differences between probabilistic and deterministic AI will help manufacturers make more informed choices and achieve measurable results. As professionals become interested in using ...

Probabilistic programming is an approach to computing based on the idea that probabilistic models can be naturally and efficiently represented as executable code. This idea has enabled researchers to ...

Ising machines demonstrate significant potential to tackle computationally complex challenges, including combinatorial optimization problems related to logistics, manufacturing, finance, and AI. The ...

Probabilistic programming languages (PPLs) have emerged as a transformative tool for expressing complex statistical models and automating inference procedures. By integrating probability theory into ...

Functional programming, as the name implies, is about functions. While functions are part of just about every programming paradigm, including JavaScript, a functional programmer has unique ...

ABSTRACT: Statistical biases may be introduced by imprecisely quantifying background radiation reference levels. It is, therefore, imperative to devise a simple, adaptable approach for precisely ...

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 ...