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 ...

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 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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Whether you are a technology enthusiast or a professional looking to enhance your scripting skills, we have designed this Windows PowerShell scripting tutorial for beginners, especially for you. So, ...

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 ...