Bayesian statistical models use prior data to update the probability of a hypothesis as new evidence emerges. Image credit: PeopleImages / Shutterstock.com Bayesian statistical models could help ...

Abstract: Bayesian inference provides a methodology for parameter estimation and uncertainty quantification in machine learning and deep learning methods. Variational inference and Markov Chain ...

This study proposes an important new approach to analyzing cell-count data, which are often undersampled and cannot be accurately assessed using traditional statistical methods. The case studies ...

For whom? The events are open to all interested, within or outside of KI. The events are free of charge. The program is tailored towards users of statistics (but you don’t need to be a statistician), ...

While the majority of stroke researchers use frequentist statistics to analyze and present their data, Bayesian statistics are becoming more and more prevalent in stroke research. As opposed to ...

In the lead up to this year’s presidential election, Andrew Gelman, a professor of political science and statistics, collaborated with Ben Goodrich, an instructor in the political science department, ...

A set of notebooks related to convex optimization, variational inference and numerical methods for signal processing, machine learning, deep learning, graph analysis, bayesian programming, statistics ...

Bayes’s core contribution, which Chivers skillfully renders into cogent prose designed to educate the lay reader, is the notion that the likelihood of an event taking place in the future depends, in ...