Abstract: The Baum-Welch algorithm together with its derivatives and variations has been the main technique for learning hidden Markov models (HMMs) from observational data. We present an HMM learning ...

To this end, this paper uncovers dynamic spatiotemporal patterns emerging from electrically stimulated neuronal cultures using hidden Markov models (HMMs) to characterize multi-channel spike trains as ...

First, a definition so that we are all talking the same Russian. A Hidden Markov Model (HMM) is a statistical model that assumes there are underlying, unobservable (hidden) states that drive ...

Dr. Xiaomin Chen, a researcher at The University of Alabama in Huntsville, has published a paper in Geophysical Research ...

Find more information on the Altmetric Attention Score and how the score is calculated. Several studies have applied the hidden Markov model (HMM) in multimode process monitoring. However, because the ...

Autodesk Inc. reported a strong performance for the first quarter of 2025, surpassing earnings and revenue forecasts. The company’s earnings per share (EPS) stood at $2.29, higher than the ...

In fact, many measures of the computational complexity of cutting-edge foundation models are already close to those of the ... “Scenarios for the Transition to AGI.” University of Virginia working ...

As global urgency around climate change mounts, she has reported on how companies are — and are not — responding to calls for a rapid energy transition. She has reported on why a country that ...