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

Uncertainty is an intrinsic part of neural computation, whether for sensory processing, motor control or cognitive reasoning. For instance, it is impossible to determine with certainty the age of a ...

Probabilistic Graphical Models (PGMs) are a popular way of portraying condi- tional dependencies between random variables (randvars) of a complex proba- bility distribution. One of the main purposes ...

How do human beings perceive their environment and take their decisions? To successfully interact with the immediate environment, for human beings it is not enough to have basic evidence of the world ...

Many organisms can predict future events from the statistics of past experience, but humans also excel at making predictions by pure reasoning: integrating multiple sources of information, guided by ...

In the 1950s and '60s, artificial-intelligence researchers saw themselves as trying to uncover the rules of thought. But those rules turned out to be way more complicated than anyone had imagined.

The Department of Computer Science, Faculty of Science, University of Helsinki invites applications for a Doctoral or Postdoctoral Researcher in Resource-Efficient Probabilistic Machine Learning. The ...

The Department of Computer Science, Faculty of Science, University of Helsinki invites applications for a Postdoctoral Researcher in Probabilistic Machine Learning and Amortized Inference. The is an ...

Probabilistic Sentential Decision Diagrams (PSDDs) are an elegant framework for learning from and reasoning about data. They provide tractable representations of discrete probability distributions ...

Alex Burmester does not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and has disclosed no relevant affiliations beyond ...