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1 ribution or if burst arrival deviates from a Poisson process.
2 tributed and that they arrive according to a Poisson process.
3 d, is not distinguishable from a homogeneous Poisson process.
4 n successive ants returning to the nest is a Poisson process.
5 rons were less variable than expected from a Poisson process.
6 able signal: noise ratio than predicted by a Poisson process.
7 s (10-55 ms) more often than expected from a Poisson process.
8 ry process, rather than as a highly variable Poisson process.
9 for the 95% confidence limits expected for a Poisson process.
10 aced onto a phylogenetic tree according to a Poisson process.
11 vary across lineages according to a compound Poisson process.
12  described adequately by a simple stochastic Poisson process.
13  of DNMs and those predicted by a stochastic Poisson process.
14 ibuted in time and thus well approximated by Poisson processes.
15 lustering the sample paths of nonhomogeneous Poisson processes.
16 of an ensemble of independent rate-modulated Poisson processes.
17                         Standard results for Poisson processes allow key computations to be decoupled
18 complexes that are randomly distributed as a Poisson process among the population of granules.
19       We modeled the dynamics as a two-state Poisson process and calculated the kinetic rates from th
20 trains of stimuli to motor nerves timed as a Poisson process and coherence analysis, we also examined
21                         Core teams form by a Poisson process and produce a Poisson distribution of te
22 individuals make contacts at the points of a Poisson process and then transmit infection along these
23 ws nodes to arrive in batches according to a Poisson process and to form hyperedges with existing bat
24 tion number, R(t), modelling the system as a Poisson process and using Markov Chain Monte-Carlo.
25  the non-mutated positions, according to the Poisson process and/or the Taylor series.
26 ndomly within a sequence, then they follow a Poisson process, and a histogram of the number of observ
27 ow contrasts was greater than predicted by a Poisson process, and at high contrasts the responses wer
28 e count variability was lower than that of a Poisson process at all three stages but increased at eac
29 embles were well described as rate-modulated Poisson processes but with very high precision (approxim
30 hat vesicles are released independently by a Poisson process, but this does not hold at ribbon-type s
31 shown to be exactly represented by a spatial Poisson process combined with independent tracer-swimmer
32 eling the sampling times as an inhomogeneous Poisson process dependent on effective population size.
33                                  A two-state Poisson process describes the interdomain dynamics, wher
34 ata on activity distributions to ensure that Poisson processes do not distort the underlying LN distr
35                                  Inputs were Poisson process-driven excitatory and inhibitory synapti
36                       Modeling dynamics as a Poisson process enables connecting the picosecond timesc
37 s for bursty activity, and a non-homogeneous Poisson process for longer inactivity between bursts.
38  to peripheral stimulation is simulated by a Poisson process generating nerve fiber spike trains at v
39           Although the fractal-Gaussian-rate Poisson process has not been proven to have better agree
40 e spike generation process was modelled as a Poisson process in which depolarizing events summate and
41 y visual cortex are well fit by a mixture of Poisson processes; in this special case, our computation
42           In this analysis, an inhomogeneous Poisson process (IPP) modeling approach is adopted from
43                    The fractal-Gaussian-rate Poisson process is compared and contrasted with previous
44 jective, we developed a marked inhomogeneous Poisson process model that allows us to incorporate both
45 gs of the concerted changes closely follow a Poisson process model, and the sound transition networks
46  by mutational types is closely connected to Poisson process models of crystallization, which we exte
47 nalysis of SCR events, which do not follow a Poisson process observed in other eukaryotic cells.
48 uch responses was smaller than expected from Poisson processes, often reaching the theoretical minimu
49 s, but it has a global characterization as a Poisson process on the phylogeny.
50 t were not spatial random (i.e., homogeneous Poisson process) or regular but, instead, exhibited stro
51 ving rise to independent mutant gametes in a Poisson process, or before meiosis, giving rise to multi
52 ently probabilistic, and can be modeled as a Poisson process over short time scales.
53 he data) is incorrect, and (ii) the compound Poisson process prior model (which describes the prior d
54 namics is well captured by a space-dependent Poisson process resulting from the space-dependent motio
55 d bacterial ssu rRNA's are consistent with a Poisson process since last common ancestor.
56          Emergency studies were modeled as a Poisson process; slots were reserved such that rate of r
57  the tracer follows a non-Markovian coloured Poisson process that accounts for all empirical observat
58        We propose a cascading nonhomogeneous Poisson process that explicitly integrates these periodi
59 l spike trains are approximately independent Poisson processes, that correlations among them can be l
60                             An inhomogeneous Poisson process, the standard model for partitioning fir
61 meaningfully compared to expectations from a Poisson process, the test does not permit calculations o
62 dels of molecular evolution, including other Poisson processes, the fractal renewal process, a Levy-s
63                            And, by using the Poisson process theory and a continuity technique, the h
64 e distribution of the model by employing the Poisson process theory and the characteristic equation.
65                                          The Poisson process theory is used to describe the arrival p
66 pisodic models such as the doubly stochastic Poisson process, this model accounts for the large varia
67                          We used a two-state Poisson process to describe the dynamics and calculate t
68   ELLA uses an over-dispersed nonhomogeneous Poisson process to model spatial count data with a unifi
69                        We use nonhomogeneous Poisson processes to model the acquisition of parasites,
70 ions consistent with a fractal-Gaussian-rate Poisson process, which assumes common descent without as
71 king activity is modeled as an inhomogeneous Poisson process whose instantaneous rate is a function o
72 d a model in which spikes are generated by a Poisson process whose rate is the product of a drive tha
73 iking activity using a fractal inhomogeneous Poisson process with dynamic rate, which is the product
74 topping the beach can be modeled as a marked Poisson process with exponentially distributed sizes or
75 of both constitutive and periodic genes is a Poisson process with transcription rates scaling with ce