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

Researchers can demonstrate that on some standard computer-vision tasks, short programs -- less than 50 lines long -- written in a probabilistic programming language are competitive with conventional ...

A collaboration including the University of Oxford, University of British Columbia, Intel, New York University, CERN, and the National Energy Research Scientific Computing Center is working to make it ...

This online data science specialization is designed to provide you with a solid foundation in probability theory in preparation for the broader study of statistics. The specialization also introduces ...

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 Virtual Brain Inference (VBI) toolkit enables efficient, accurate, and scalable Bayesian inference over whole-brain network models, improving parameter estimation, uncertainty quantification, and ...

Both humans and other animals are good at learning by inference, using information we do have to figure out things we cannot observe directly. New research from the Center for Mind and Brain at the ...