Joint distribution of first exit times of a two dimensional Wiener process with jumps with application to a pair of coupled neurons

2013 ◽  
Vol 245 (1) ◽  
pp. 61-69 ◽  
Author(s):  
Laura Sacerdote ◽  
Cristina Zucca
1988 ◽  
Vol 20 (4) ◽  
pp. 822-835 ◽  
Author(s):  
Ed Mckenzie

A family of models for discrete-time processes with Poisson marginal distributions is developed and investigated. They have the same correlation structure as the linear ARMA processes. The joint distribution of n consecutive observations in such a process is derived and its properties discussed. In particular, time-reversibility and asymptotic behaviour are considered in detail. A vector autoregressive process is constructed and the behaviour of its components, which are Poisson ARMA processes, is considered. In particular, the two-dimensional case is discussed in detail.


2015 ◽  
Vol 300 ◽  
pp. 862-886 ◽  
Author(s):  
Per Lötstedt ◽  
Lina Meinecke

1998 ◽  
Vol 54 (6) ◽  
pp. 1230-1244 ◽  
Author(s):  
Alexandra Goldstein ◽  
Kam Y. J. Zhang

The joint distribution of electron density and its gradient in a protein electron-density map was examined. This joint distribution was represented by a two-dimensional histogram (2D histogram) of electron-density values and the modulus of the gradient. 16 structures representing distinct protein-fold families were selected to study the dependence of the 2D histogram on resolution, overall temperature factor, structural conformation and phase error. The similarity between the histograms for a pair of structures was measured by correlation coefficient, and the residual provided a measure of the difference. The 2D histogram was found to vary with resolution and overall temperature factor, but was found to be insensitive to structure conformation. The average correlation coefficient between pairs of 2D histograms at three different resolutions examined was 0.90 with a standard deviation of 0.04. The average residual for the same condition was 0.13 with a standard deviation of 0.03. The 2D histogram was also found to be sensitive to phase error. The average correlation coefficient and residual between 2D histograms with 10° phase difference are 0.71 and 0.18, respectively. The variation of the 2D histogram resulting from structure-conformation changes was estimated to be equivalent to that of a 4° phase error. This establishes the minimal phase error that a 2D histogram-matching method could achieve. The conservation of the 2D histogram with respect to structure conformation enables the prediction of the ideal 2D histogram for unknown structures. The sensitivity of the 2D histogram to phase error suggests that it could be used as a target for the density-modification method and also could be used as a figure of merit for phase selection in ab initio phasing.


2014 ◽  
Vol 63 (23) ◽  
pp. 230204
Author(s):  
Zuo Dong-Dong ◽  
Hou Wei ◽  
Yan Peng-Cheng ◽  
Feng Tai-Chen

2020 ◽  
Vol 54 (3) ◽  
pp. 811-844
Author(s):  
Samuel Herrmann ◽  
Cristina Zucca

The simulation of exit times for diffusion processes is a challenging task since it concerns many applications in different fields like mathematical finance, neuroscience, reliability… The usual procedure is to use discretization schemes which unfortunately introduce some error in the target distribution. Our aim is to present a new algorithm which simulates exactly the exit time for one-dimensional diffusions. This acceptance-rejection algorithm requires to simulate exactly the exit time of the Brownian motion on one side and the Brownian position at a given time, constrained not to have exit before, on the other side. Crucial tools in this study are the Girsanov transformation, the convergent series method for the simulation of random variables and the classical rejection sampling. The efficiency of the method is described through theoretical results and numerical examples.


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