Towards Reinforcement Learning-based Flow Space OptimizationA Deep Paradigm Shift in Modern Bayesian Inference — From the Limits of MCMC/Variational Inference to Neural Processes and GFlowNetsIntroduc ...

Approximate Bayesian computation (ABC) constitutes a family of likelihood-free methods that have emerged as a cornerstone in statistical inference for complex models where evaluation of the likelihood ...

Introduction About six months ago, I hit a wall while reviewing the results of an internal A/B test. When I presented the ...

This course introduces the theoretical, philosophical, and mathematical foundations of Bayesian Statistical inference. Students will learn to apply this foundational knowledge to real-world data ...

This paper develops new econometric methods to infer hospital quality in a model with discrete dependent variables and non-random selection. Mortality rates in patient discharge records are widely ...

Get your news from a source that’s not owned and controlled by oligarchs. Sign up for the free Mother Jones Daily. It is really, really hard to find stuff to write about other than the C19 pandemic.

According to GoogleDeepMind, Zoubin Ghahramani explains how uncertainty and probability make real‑world AI decisions safer and more reliable.

Bayesian networks have become popular tools for enterprise data scientists working with prediction, as the rise of cheap and abundant cloud computing has made way for adaptable infrastructure. In the ...