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1 ments (i.e., the whole spectrum modeled as a probability density function).
2 ysema map was calculated based on the fitted probability density function.
3 quantifies gene-set activity with a complete probability density function.
4 tat quality has either a uniform or a linear probability density function.
5 i.i.d.) draws does not come from a specified probability density function.
6 ental plots of mean square displacements and probability density function.
7 haracterised by strong memory effects in the probability density function.
8 and quantifying gene set activity as a full probability density function.
9 e presented in a manner similar to that of a probability density function.
10 arious moments of the reduced global turning probability density function.
11 ing alpha and beta ranges, encodes the event probability density function.
12 and turning angles are typically reported as probability density functions.
14 timator is one that uses (1/m2) as the prior probability density function and a quadratic loss functi
17 capture observed ENSO statistics such as the probability density function and power spectrum of easte
18 carried out not on probabilities but on (1) probability density functions and (2) these probability
20 s are stochastically governed by independent probability density functions; and 2), a finite Hookian
22 probability density functions and (2) these probability density functions are derived from samples.
24 the scheme are a representation of each new probability density function by means of a set of functi
25 ations are shown, including evolution of the probability density function, calculation of closure pro
26 by incorporating a central tendency prior, a probability density function centered at the mean durati
27 Y PET/CT data were combined with microscopic probability-density functions describing microsphere clu
30 amework that allows the determination of the probability density function for a stochastic process be
31 licit expression of a reduced global turning probability density function for motile bacteria was der
33 at of intra-shell edges; and 4) the distance probability density function for the afferent connection
36 e-evolution of the moments of the univariate probability density functions for junctional SR [Ca2+] j
38 standard statistical tests of the recovered probability density functions for the measured observabl
39 e a general exponential decaying law for the probability density function governing the number of con
40 our length for loop formation as well as the probability density functions have been found to be stro
42 first peak as well as the first well in the probability density functions increases with the size of
43 ffs, are found to be well characterized by a probability density function involving only two paramete
45 ittency in terms of power availability and a probability density function is further employed to inve
46 f experimental porosimetry data, a pore size probability density function is introduced to represent
48 and controller and a predefined ideal joint probability density function is used to characterise the
51 A central tenet of this argument is that the probability density function of DNA methylation informat
55 de) and the natural logarithm of mode on the probability density function of neutral facial expressio
56 ocin on the natural logarithm of mode on the probability density function of neutral facial expressio
57 e increased natural logarithm of mode on the probability density function of neutral facial expressio
59 al of a model form for the conditional joint probability density function of predator and prey veloci
60 we derived an integral equation relating the probability density function of source strengths, f alph
61 n each case were evaluated by constructing a probability density function of the action potential dur
62 , we derive an analytical expression for the probability density function of the fraction of vesicle
64 s to develop a theoretical model that is the probability density function of the morphometric lengths
66 is showed that microdosimetric spectrum (the probability density function of the stochastic physical
67 -Leibler Divergence between the actual joint probability density function of the system dynamics and
70 a new data processing method to extract the probability density functions of the diffusion coefficie
71 simulations results, we show that the joint probability density functions of the second and third in
74 form of probability distributions (e.g., the probability density function over the orientation of a c
75 , we estimate the tail exponent alpha of the probability density function P(|R|) approximately |R|(-1
78 subset of three stations with long records, probability density function (PDF) analyses of the 95% p
80 ng of gasoline particulate filters (GPFs), a probability density function (PDF) based heterogeneous m
82 size in which an explicit solution of an FPT probability density function (PDF) exists for the first
84 on describing the stochastic dynamics of the probability density function (PDF) of barrier elevation
87 so-called area rule, according to which the probability density function (PDF) of the circulation ar
88 e crucial case of isothermal turbulence, the probability density function (PDF) of the logarithmic de
89 range of their temporal half-widths and the probability density function (PDF) of their widths resem
90 omputation that presupposes knowledge of the probability density function (pdf) of visuo-motor error
93 e differences in the behaviours of the joint probability density functions (PDFs) between second and
97 stigate both assumptions by presenting event probability density functions (PDFs) in each of three se
99 thereby provide compelling evidence that the probability density functions (PDFs) of a fully develope
105 tions should be fitted by a t location-scale probability density function rather than by a normal dis
106 mans anticipate future events by calculating probability density functions, rather than hazard rates.
107 umber, volume, and mass concentrations using probability density functions that represent environment
109 observed array-CGH signal as sampling from a probability-density function, uses a kernel-based approa
111 ing process and the transformation rules for probability density functions, we develop adjustments fo
112 te have a very wide ranges of values, with a probability density function well approximated by a powe
113 duced uncorrelated variables in T, where the probability density functions were approximated as norma
114 and single-cell metabolic rates described by probability density functions were randomly generated in
115 es of any stock market is characterized by a probability density function, which is a simple power la
117 model parameters as quantified by posterior probability density functions, which is useful for compa
118 re are assumed to be distributed by Gaussian probability density functions, whose center peaks are lo
119 stribution is found to follow a finite-width probability density function with certain skewness which