Data science and machine learning algorithms can help us form probabilistic forecasts of things like sporting events.

It feels like there’s no escaping AI right now, whether you’re trying to type a sentence without being interrupted by a ...

Abstract: In this paper, we investigate the sampling rate mismatch problem in distributed microphone arrays and propose a correlation maximization algorithm to blindly estimate the sampling rate ...

If you use these materials for teaching or research, please use the following citation: Rhoads, S. A. (2023). pyEM: Expectation Maximization with MAP estimation in ...

The G-SPECT acquisition of a dynamic heart phantom simulating the beating left ventricle (BSI) was obtained at the Department of Nuclear Medicine, Center Hospitalier Universitaire Vaudois and ...

Researchers have developed a new forecasting model that helps companies more accurately estimate how many customers are interested in a product -- even when key data is missing. The study introduces a ...

This paper shows that the Expectation-Maximization (EM) algorithm for regime-switching dynamic factor models provides satisfactory performance relative to other estimation methods and delivers a good ...

Probabilistic models, such as hidden Markov models or Bayesian networks, are commonly used to model biological data. Much of their popularity can be attributed to the existence of efficient and robust ...

ABSTRACT: Compositional data, such as relative information, is a crucial aspect of machine learning and other related fields. It is typically recorded as closed data or sums to a constant, like 100%.

ABSTRACT: This paper is concerned about studying modeling-based methods in cluster analysis to classify data elements into clusters and thus dealing with time series in view of this classification to ...