Results 1 to 10 of about 3,983,437 (321)
A common goodness-of-fit framework for neural population models using marked point process time-rescaling. [PDF]
J Comput Neurosci, 2018 A critical component of any statistical modeling procedure is the ability to assess the goodness-of-fit between a model and observed data. For spike train models of individual neurons, many goodness-of-fit measures rely on the time-rescaling theorem and ...
Tao L, Weber KE, Arai K, Eden UT.
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A Marked Point Process Framework for Extracellular Electrical Potentials [PDF]
Frontiers in Systems Neuroscience, 2017 Neuromodulations are an important component of extracellular electrical potentials (EEP), such as the Electroencephalogram (EEG), Electrocorticogram (ECoG) and Local Field Potentials (LFP).
Carlos A. Loza +2 more
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Design as a Marked Point Process [PDF]
Journal of Mechanical Design, 2021 Abstract Although artificial intelligence (AI) systems which support composition using predictive text are well established, there are no analogous technologies for mechanical design. Motivated by the vision of a predictive system that learns from previous designs and can interactively provide a list of established feature alternatives ...
Quigley, John +4 more
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A MARKED POINT PROCESS MODEL FOR VEHICLE DETECTION IN AERIAL LIDAR POINT CLOUDS [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2012 In this paper we present an automated method for vehicle detection in LiDAR point clouds of crowded urban areas collected from an aerial platform.
A. Börcs, C. Benedek
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Marked point process variational autoencoder with applications to unsorted spiking activities. [PDF]
PLoS Computational BiologySpike train modeling across large neural populations is a powerful tool for understanding how neurons code information in a coordinated manner. Recent studies have employed marked point processes in neural population modeling. The marked point process is
Ryohei Shibue, Tomoharu Iwata
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A stochastic marked point process model for earthquakes [PDF]
Natural Hazards and Earth System Sciences, 2003 A simplified stochastic model for earthquake occurrence focusing on the spatio-temporal interactions between earthquakes is presented. The model is a marked point process model in which each earthquake is represented by its magnitude and coordinates ...
L. Holden, S. Sannan, H. Bungum
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A Marked Point Process Filtering Approach for Tracking Sympathetic Arousal From Skin Conductance
IEEE Access, 2020 Human emotion represents a complex neural process within the brain. The ability to automatically recognize emotions from physiological signals has the potential to impact humanity in multiple ways through applications in human-machine interaction, remote
Dilranjan S. Wickramasuriya +1 more
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FACADE INTERPRETATION USING A MARKED POINT PROCESS [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2016 Our objective is the interpretation of facade images in a top-down manner, using a Markov marked point process formulated as a Gibbs process. Given single rectified facade images, we aim at the accurate detection of relevant facade objects as windows and
S. Wenzel, W. Förstner
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Marginal mark regression analysis of recurrent marked point process data. [PDF]
Biometrics, 2009 SummaryLongitudinal studies typically collect information on the timing of key clinical events and on specific characteristics that describe those events. Random variables that measure qualitative or quantitative aspects associated with the occurrence of an event are known as marks.
French B, Heagerty PJ.
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Decoding position from multiunit activity using a marked point process filter [PDF]
BMC Neurosci, 2015 Traditionally, experiments designed to study the role of specific spike patterns in learning and memory tasks take one of two forms, 1) observational studies that characterize statistical properties of neural activity during such tasks or 2 ...
Deng X, Liu D, Kay K, Frank L, Eden U.
europepmc +2 more sources