Citizen science projects are vehicles for democratizing science, giving ordinary people opportunities to advance scientific knowledge by collecting data, reporting observations, and conducting ...

This 4 day course introduces academics and professional data analysts to Bayesian inference, using the Stan interface in R. The atmosphere of the workshop will be friendly and supportive, with the ...

From advances in artificial intelligence and blockchain to precision agriculture, statistical innovation drives progress and bolsters U.S. competitiveness. Data underpins science in every field and ...

The Society for Financial Econometrics Lecture Series features bi-annual SoFiE-invited lectures by leading scholars in financial econometrics. The Society for Financial Econometrics has created a new ...

This course focuses on the principles of Bayesian analysis with the aim to enable participants to apply Bayesian methods on their own research and understand other people's results via Bayesian ...

This repository contains lecture materials for a one-hour introduction to Simulation-Based Inference (SBI) in cosmology, presented at the Les Houches Summer School on Dark Universe at the Les Houches ...

Probability theory is indispensable in computer science: It is at the core of artificial intelligence and machine learning, which require decision making under uncertainty. It is integral to CS theory ...

Dormancy is a widespread bet-hedging strategy across taxa, enabling organisms to survive natural and anthropogenic disturbances. It fundamentally alters eco-evolutionary processes, including ...

We will start at 2pm on Tuesday (registration from 1pm). There will be a reception on Tuesday evening and a dinner on Thursday. Wednesday evening is free — there are plenty of restaurants near South ...

Some of the material on this web page is based upon work supported by the National Science Foundation under Grants SES-0350686, SES-0719055, and . Any opinions, findings and conclusions or ...

Understanding the interplay between network architecture, dataset statistics, and learning algorithms is a key challenge in deep learning. We overcome this challenge analytically for zero-noise ...