Results 1 to 10 of about 2,877,510 (222)
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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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.
europepmc +4 more sources
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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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.
europepmc +4 more sources
Hybrid Marked Point Processes: Characterization, Existence and Uniqueness [PDF]
Market Microstructure and Liquidity, 2018 In this paper, we introduce a class of hybrid marked point processes, which encompasses and extends continuous-time Markov chains and Hawkes processes. While this flexible class amalgamates such existing processes, it also contains novel processes with complex dynamics.
Maxime Morariu-Patrichi +1 more
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The Annals of Statistics, 2007 Published at http://dx.doi.org/10.1214/009053606000001163 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)
McElroy, T. +3 more
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Fingerprint Analysis with Marked Point Processes [PDF]
, 2014 We present a framework for fingerprint matching based on marked point process models. An efficient Monte Carlo algorithm is developed to calculate the marginal likelihood ratio for the hypothesis that two observed prints originate from the same finger against the hypothesis that they originate from different fingers. Our model achieves good performance
Forbes, Peter G. M. +2 more
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PtProcess: An R Package for Modelling Marked Point Processes Indexed by Time [PDF]
Journal of Statistical Software, 2010 This paper describes the package PtProcess which uses the R statistical language. The package provides a unified approach to fitting and simulating a wide variety of temporal point process or temporal marked point process models. The models are specified
David Harte
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