Quantifying Statistical Interdependence by Message Passing on Graphs—Part II: Multidimensional Point Processes

DOI10.1162/neco.2009.11-08-899zbMath1402.92015OpenAlexW2493726763WikidataQ51834615 ScholiaQ51834615MaRDI QIDQ5323762

Justin Dauwels, Toshimitsu Musha, Theophane Weber, Andrzej Cichocki, Francois-Benoit Vialatte

Publication date: 30 July 2009

Published in: Neural Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1162/neco.2009.11-08-899

zbMATH Keywords

stochastic event synchronypairwise alignment

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics to biology and medical sciences; meta analysis (62P10) General biostatistics (92B15) Neural biology (92C20)

Related Items (1)

Quantifying Statistical Interdependence, Part III: N > 2 Point Processes

Cites Work

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• A machine learning approach to the analysis of time-frequency maps, and its application to neural dynamics
• Local convergence analysis of a grouped variable version of coordinate descent
• Wave atoms and sparsity of oscillatory patterns
• Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information
• Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit
• Asymptotic wavelet and Gabor analysis: extraction of instantaneous frequencies
• Theoretical Improvements in Algorithmic Efficiency for Network Flow Problems
• The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis
• New tight frames of curvelets and optimal representations of objects with piecewise C² singularities
• Matching pursuits with time-frequency dictionaries
• Compressed sensing

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