Change-point detection in time series analysis comprises a suite of statistical and computational techniques aimed at identifying times at which the probabilistic structure of sequential observations ...

This is a collection of algorithms and models written in Python for probabilistic programming. The main focus of the package is on Bayesian reasoning by using Bayesian networks, Markov networks, and ...

Abstract: The problem of sequential change diagnosis is considered, where a sequence of independent random elements is accessed sequentially, there is an abrupt change in its distribution at some ...

Abstract: In this article, series arc fault detection and identification is investigated for dc microgrids using a statistical model based on nodal analysis. The consecutive sample difference of the ...

The full probability formula and Bayes' formula are important formulas in probability theory, mainly used to calculate the probability of more complex events, and they are essentially a combination of ...

Hospitals across the country are using software powered by algorithms with racial biases, according to a new report from a coalition of healthcare providers. This can cause physicians to misdiagnose ...

International Journal for Quality in Health Care, Vol. 14, No. 3 (June 2002), pp. 251-258 (8 pages) Background. Quality assurance of medical practice requires assessment of doctors' performance, ...

An internal Facebook report found that the social media platform’s algorithms – the rules its computers follow in deciding the content that you see – enabled disinformation campaigns based in Eastern ...

Data-Driven Thinking” is written by members of the media community and contains fresh ideas on the digital revolution in media. Today’s column is written by AJ Brown, CEO and co-founder of LeadsRx.