Results 231 to 240 of about 101,818 (290)

An intelligent diagnosis method for PMSM radial misalignment based on current signal and Swin-BiGRU multimodal fusion. [PDF]

Sci Rep
Yu H, Guo J, Zhang H.
europepmc   +1 more source

Weather and Climate Extremes: Simplex, Dynamical Systems and Hull Clustering

Journal of Geophysical Research: Atmospheres, Volume 131, Issue 4, 28 February 2026.
Abstract A novel method is developed and applied to identify high‐dimensional weather and climate extremes located on the envelope of the data set within its state space. The method is based on formulating and integrating dynamical systems whose attractive set, that is, stable fixed points, is constituted of extreme states residing on the convex hull ...
A. Hannachi, K. Finke, N. Trendafilov, D. Monselesan, J. Risbey, C. Chapman, L. Chafik   +6 more
wiley   +1 more source

Learning in Probabilistic Boolean Networks via Structural Policy Gradients. [PDF]

Entropy (Basel)
Rivera Torres PJ.
europepmc   +1 more source

Charting the structure-sequence landscape of light chain amyloids. [PDF]

Bioinformatics
Orlando G, Gallardo R, Colla A, Schymkowitz J, Rousseau F.   +4 more
europepmc   +1 more source

The asymmetry system properties of the visual system: Evidence from serial face processing. [PDF]

J Vis
Yu JM, Ying H.
europepmc   +1 more source

Hidden Markov Trajectories of Early-Adolescent Media Overdependence and Machine Learning Prediction of High-Risk Maintenance from Early Childhood and Lower Elementary Predictors. [PDF]

Behav Sci (Basel)
Goh EK, Kyun J.
europepmc   +1 more source

Protocol for using treeLFA to infer multimorbidity patterns in the form of disease topics from diagnosis data in biobanks. [PDF]

STAR Protoc
Zhang Y, Jiang X, McVean G, Lunter G.
europepmc   +1 more source

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Filtering hidden semi-Markov chains

Statistics & Probability Letters, 2013
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Elliott, R., Limnios, N., Swishchuk, A.
openaire   +4 more sources

Hidden Markov Chains

2013
Hidden Markov models (HMMs) have been applied to many real-world applications. Usually HMMs only deal with the first-order transition probability distribution among the hidden states, see for instance Sect.1.4. Moreover, the observable states are affected by the hidden states but not vice versa. In this chapter, we study both higher-order hidden Markov
Wai-Ki Ching, Ximin Huang, Michael K. Ng, Tak-Kuen Siu   +3 more
openaire   +1 more source