scholarly journals Mechanistic spatio-temporal point process models for marked point processes, with a view to forest stand data

Biometrics ◽  
2015 ◽  
Vol 72 (3) ◽  
pp. 687-696 ◽  
Author(s):  
Jesper Møller ◽  
Mohammad Ghorbani ◽  
Ege Rubak
Keyword(s):  
Point Process ◽  
Point Processes ◽  
Marked Point ◽  
Forest Stand ◽  
Process Models ◽  
Marked Point Processes ◽  
Point Process Models ◽  
Spatio Temporal

Marked point processes as limits of Markovian arrival streams

1993 ◽  
Vol 30 (02) ◽  
pp. 365-372 ◽  
Author(s):  
Søren Asmussen ◽  
Ger Koole
Keyword(s):  
Point Process ◽  
Point Processes ◽  
Marked Point ◽  
The State ◽  
Marked Point Processes ◽  
Marked Point Process ◽  
State Transitions ◽  
Poisson Arrival ◽  
Convergence Of Moments ◽  
Environmental Process

A Markovian arrival stream is a marked point process generated by the state transitions of a given Markovian environmental process and Poisson arrival rates depending on the environment. It is shown that to a given marked point process there is a sequence of such Markovian arrival streams with the property that as m →∞. Various related corollaries (involving stationarity, convergence of moments and ergodicity) and counterexamples are discussed as well.

Marked point processes as limits of Markovian arrival streams

1993 ◽  
Vol 30 (2) ◽  
pp. 365-372 ◽  
Author(s):  
Søren Asmussen ◽  
Ger Koole
Keyword(s):  
Point Process ◽  
Point Processes ◽  
Marked Point ◽  
The State ◽  
Marked Point Processes ◽  
Marked Point Process ◽  
State Transitions ◽  
Poisson Arrival ◽  
Convergence Of Moments ◽  
Environmental Process

A Markovian arrival stream is a marked point process generated by the state transitions of a given Markovian environmental process and Poisson arrival rates depending on the environment. It is shown that to a given marked point process there is a sequence of such Markovian arrival streams with the property that as m →∞. Various related corollaries (involving stationarity, convergence of moments and ergodicity) and counterexamples are discussed as well.

scholarly journals Understanding species distribution in dynamic populations: a new approach using spatio‐temporal point process models

Ecography ◽  
2019 ◽  
Vol 42 (6) ◽  
pp. 1092-1102 ◽  
Author(s):  
Andrea Soriano‐Redondo ◽  
Charlotte M. Jones‐Todd ◽  
Stuart Bearhop ◽  
Geoff M. Hilton ◽  
Leigh Lock ◽  
...  
Keyword(s):  
Point Process ◽  
Species Distribution ◽  
Process Models ◽  
New Approach ◽  
Point Process Models ◽  
Spatio Temporal ◽  
Dynamic Populations

scholarly journals Clusterless Decoding of Position from Multiunit Activity Using a Marked Point Process Filter

2015 ◽  
Vol 27 (7) ◽  
pp. 1438-1460 ◽  
Author(s):  
Xinyi Deng ◽  
Daniel F. Liu ◽  
Kenneth Kay ◽  
Loren M. Frank ◽  
Uri T. Eden
Keyword(s):  
Real Time ◽  
Point Process ◽  
Point Processes ◽  
Marked Point ◽  
Marked Point Processes ◽  
Marked Point Process ◽  
Spike Sorting ◽  
Decoding Algorithm ◽  
Population Activity ◽  
Spiking Activity

Point process filters have been applied successfully to decode neural signals and track neural dynamics. Traditionally these methods assume that multiunit spiking activity has already been correctly spike-sorted. As a result, these methods are not appropriate for situations where sorting cannot be performed with high precision, such as real-time decoding for brain-computer interfaces. Because the unsupervised spike-sorting problem remains unsolved, we took an alternative approach that takes advantage of recent insights into clusterless decoding. Here we present a new point process decoding algorithm that does not require multiunit signals to be sorted into individual units. We use the theory of marked point processes to construct a function that characterizes the relationship between a covariate of interest (in this case, the location of a rat on a track) and features of the spike waveforms. In our example, we use tetrode recordings, and the marks represent a four-dimensional vector of the maximum amplitudes of the spike waveform on each of the four electrodes. In general, the marks may represent any features of the spike waveform. We then use Bayes’s rule to estimate spatial location from hippocampal neural activity. We validate our approach with a simulation study and experimental data recorded in the hippocampus of a rat moving through a linear environment. Our decoding algorithm accurately reconstructs the rat’s position from unsorted multiunit spiking activity. We then compare the quality of our decoding algorithm to that of a traditional spike-sorting and decoding algorithm. Our analyses show that the proposed decoding algorithm performs equivalent to or better than algorithms based on sorted single-unit activity. These results provide a path toward accurate real-time decoding of spiking patterns that could be used to carry out content-specific manipulations of population activity in hippocampus or elsewhere in the brain.

scholarly journals MODELLING THE POSITION OF CELL PROFILES ALLOWING FOR BOTH INHOMOGENEITY AND INTERACTION

2011 ◽  
Vol 19 (3) ◽  
pp. 183 ◽  
Author(s):  
Linda Stougaard Nielsen
Keyword(s):  
Point Process ◽  
Mucous Membrane ◽  
Point Processes ◽  
Point Interaction ◽  
Process Models ◽  
Markov Point Processes ◽  
Point Process Models

It is of interest to consider models for point processes that allow for interaction between the points as well as for inhomogeneity in the intensity of the points. Markov point process models are very useful to describe point interaction and can also be used to describe inhomogeneity. A particular type of inhomogeneous Markov point processes obtained by transforming a homogeneous Markov point process will be considered. The position of cell proles in a 2D section of the mucous membrane in the stomach of a rat will be examined using this model.

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