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 ...
When you’re programming an artificial intelligence application, you’re usually building statistical models that output discrete values. Is that image a human face? Whose face is it? Is that face ...
The Bayesian method is the natural approach to inference, yet it is hidden from readers behind chapters of slow, mathematical analysis. The typical text on Bayesian inference involves two to three ...
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 ...
Probabilistic programming languages (PPLs) have emerged as a transformative tool for expressing complex statistical models and automating inference procedures. By integrating probability theory into ...
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 ...
1 Dipartimento di Matematica e Informatica, Università di Ferrara, Ferrara, Italy 2 Dipartimento di Ingegneria, Università di Ferrara, Ferrara, Italy The field of Probabilistic Logic Programming (PLP) ...
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