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 ...

Probabilistic programming has emerged as a powerful paradigm that integrates uncertainty directly into computational models. By embedding probabilistic constructs into conventional programming ...

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

Abstract: Causal inference is an important field in data science and cognitive artificial intelligence. It requires the construction of complex probabilistic models to describe the causal ...

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 ...

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 ...

We consider the computable content of several key theorems in probability theory, and discuss their implications for the design of probabilistic programming languages. A random variable is said to be ...

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Generative models of tabular data are key in Bayesian analysis, probabilistic machine learning, and fields like econometrics, healthcare, and systems biology. Researchers have developed methods to ...

Abstract: Probabilistic programming languages rely fundamentally on some notion of sampling, and this is doubly true for probabilistic programming languages which perform Bayesian inference using ...