ライフサイエンスコーパス: probability density

1 step followed by integration of a posterior probability density.

2 like correlated parameters and a generalized probability density.

3 like correlated parameters and a generalized probability density.

4 ocal hidden variables and a large variety of probability densities.

5 imaging of the macroscopic exciton-polariton probability densities.

6 ed on summed archaeological radiocarbon date probability densities.

7 hase information, from measured single-state probability densities.

8 ciations between the variables and the joint probability density allows one to ask arbitrary probabil

9 simulated voltage-clamp protocols, both the probability density and Monte Carlo approaches to modeli

10 tive distribution function for the specified probability density and the empirical cumulative distrib

11 probability density, the joint steady-state probability density, and the mean first passage time.

12 deling Ca2+ release unit activity using this probability density approach avoids the computationally

13 We show how the probability density approach described here can be gener

14 The probability density approach is benchmarked and validate

15 The probability density approach is validated for a physiolo

16 The probability density approach shows that such minimal Ca(

17 In prior work, we introduced a probability density approach to modeling local control o

18 We present a probability density approach to modeling localized Ca2+

19 Using the probability density approach, we show that the time scal

20 n finite difference scheme and the resulting probability densities are coupled to ordinary differenti

21 rovides a simple solution for the transition probability density at short times, including drift, sel

22 ed on a Cantor-like set of zero measure: the probability density becomes divergent, almost everywhere

23 However, a probability density calculation can be significantly fas

24 In contrast, the symptom duration probability density curve was not broad, but instead pea

25 ometabolic diseases by comparing the overall probability density curves across township-sex strata.

26 These results demonstrate that this new probability density distribution based method is effecti

27 The angular turning probability density distribution for motile bacteria is

28 ctural models, and uses them to estimate its probability density distribution for quality assessment.

29 tions that convert the local angular turning probability density distribution into a global one.

30 ethod to solve equations describing the full probability density distribution of neuronal membrane po

31 a trivial knot, average gyration radius, and probability density distributions as functions of gyrati

32 We derive joint probability density distributions for three key uncertai

33 ibits an approximately power-law range where probability density drops slowly with each order of magn

34 p)/4bL+ cdots, three dots, centered ) is the probability density for the end-to-end vector of the fle

35 3d(0)(2)/4l(p)bL)(1 - 5l(p)/4bL +...) is the probability density for the end-to-end vector of the lin

36 rence uses a coalescent model that defines a probability density for the genealogy of randomly sample

37 Recently, it was reported that the probability density for the inter-event time tau between

38 We obtain a probability density for the slip distribution, which var

39 The distributions were then partitioned into probability density fractions according to time interval

40 The number of bins used to estimate the probability density function (pdf) affects the speed and

41 subset of three stations with long records, probability density function (PDF) analyses of the 95% p

42 The probability density function (pdf) and cumulative densit

43 ng of gasoline particulate filters (GPFs), a probability density function (PDF) based heterogeneous m

44 This dynamic HMF model is based on a probability density function (PDF) description of the po

45 Either an appropriate probability density function (pdf) model for the coeffic

46 Theory for changes in the probability density function (pdf) of precipitation accu

47 To guarantee it, the type of probability density function (pdf) of strength, includin

48 omputation that presupposes knowledge of the probability density function (pdf) of visuo-motor error

49 timator is one that uses (1/m2) as the prior probability density function and a quadratic loss functi

50 Probability density function and operator variance analy

51 capture observed ENSO statistics such as the probability density function and power spectrum of easte

52 ximate analytic expressions for the animal's probability density function are derived.

53 the scheme are a representation of each new probability density function by means of a set of functi

54 by incorporating a central tendency prior, a probability density function centered at the mean durati

55 The cell population is modeled as a probability density function F(x,t) that depends on cell

56 licit expression of a reduced global turning probability density function for motile bacteria was der

57 The probability density function for symptom duration was si

58 We estimate a probability density function for the sensitivity of clim

59 ffs, are found to be well characterized by a probability density function involving only two paramete

60 Our probability density function is constrained by objective

61 f experimental porosimetry data, a pore size probability density function is introduced to represent

62 The spatial model produced a posterior probability density function of estimated annual average

63 al of a model form for the conditional joint probability density function of predator and prey veloci

64 we derived an integral equation relating the probability density function of source strengths, f alph

65 , we derive an analytical expression for the probability density function of the fraction of vesicle

66 The probability density function of the interspike intervals

67 s to develop a theoretical model that is the probability density function of the morphometric lengths

68 The probability density function of the peak amplitudes of t

69 Unfortunately, variations in the probability density function often get smoothed over in

70 form of probability distributions (e.g., the probability density function over the orientation of a c

71 , we estimate the tail exponent alpha of the probability density function P(|R|) approximately |R|(-1

72 The GSI probability density function peaked close to 1 in early

73 tions should be fitted by a t location-scale probability density function rather than by a normal dis

74 Probability density function was fit to the joint distri

75 stribution is found to follow a finite-width probability density function with certain skewness which

76 ments (i.e., the whole spectrum modeled as a probability density function).

77 ations are shown, including evolution of the probability density function, calculation of closure pro

78 From this probability density function, P-values and confidence in

79 es of any stock market is characterized by a probability density function, which is a simple power la

80 haracterised by strong memory effects in the probability density function.

81 e presented in a manner similar to that of a probability density function.

82 arious moments of the reduced global turning probability density function.

83 ysema map was calculated based on the fitted probability density function.

84 quantifies gene-set activity with a complete probability density function.

85 tat quality has either a uniform or a linear probability density function.

86 i.i.d.) draws does not come from a specified probability density function.

87 observed array-CGH signal as sampling from a probability-density function, uses a kernel-based approa

88 ic disorder with random energy barriers, the probability density functional of the rate coefficient i

89 Probability density functions (pdf) described the variab

90 The probability density functions (PDF) of [PCr] and VO2 flu

91 Results were post-processed to yield probability density functions (PDFs) describing the circ

92 1) MEMLET fits probability density functions (PDFs) for many common dis

93 eatures such as intermittency and fat-tailed probability density functions (PDFs).

94 Ensemble-averaged probability density functions and power spectra of the i

95 Probability density functions for hitting times are eval

96 e-evolution of the moments of the univariate probability density functions for junctional SR [Ca2+] j

97 standard statistical tests of the recovered probability density functions for the measured observabl

98 our length for loop formation as well as the probability density functions have been found to be stro

99 first peak as well as the first well in the probability density functions increases with the size of

100 Probability density functions obtained for the direct ra

101 a new data processing method to extract the probability density functions of the diffusion coefficie

102 Fitting skew normal probability density functions to our data, we estimate a

103 ing process and the transformation rules for probability density functions, we develop adjustments fo

104 The resulting probability density functions, which contain the preferr

105 model parameters as quantified by posterior probability density functions, which is useful for compa

106 re are assumed to be distributed by Gaussian probability density functions, whose center peaks are lo

107 and turning angles are typically reported as probability density functions.

108 s are stochastically governed by independent probability density functions; and 2), a finite Hookian

109 Y PET/CT data were combined with microscopic probability-density functions describing microsphere clu

110 It is found that the probability densities in the SAS increase as the peaks i

111 ogical evolution is described by the flow of probability density in this curvature space that is indu

112 deep learning architecture via longitudinal probability densities inferred using Gaussian process re

113 tion in a localization transition that traps probability density into several disconnected conformati

114 For smoother boundaries, H > 1/2, the probability density is a continuous function of time.

115 e crossover from weak confinement, where the probability density is Boltzmann-like, to strong confine

116 Near the horizon, probability density is elevated at 90 degrees /270 degre

117 Above the horizon, probability density is elevated near 0 degrees slant due

118 to detect discrepancies in regions where the probability density is small in comparison with its valu

119 nsfer)-based assay and extracted the looping probability density known as the J factor (jM).

120 Probability density maps for gray matter and white matte

121 quently, univariate rather than multivariate probability densities may be employed.

122 n the values of the parameters, the lifetime probability density may have one or two peaks.

123 e calculate and compare averaged equilibrium probability densities of K(+) and Cl(-) in open-pore mod

124 Partition coefficients based on probability densities of the amino acids in each phase h

125 oped a methodology to evaluate the posterior probability densities of the response parameter space, a

126 lso derive expressions for the (conditional) probability densities of time spent in the channel by tr

127 e methods the calculation of the coalescence probability density of a genealogy requires a product ov

128 roduce two-dimensional spatial images of the probability density of an electron confined in a self-as

129 stem of advection-reaction equations for the probability density of cytosolic and luminal domain [Ca2

130 nction and the power-law distribution of the probability density of dark times imply the dynamic and

131 tion-reaction equations for the multivariate probability density of elevated cytosolic [Ca(2+)] and d

132 Here we directly probe the probability density of single quantum states of individu

133 domain) which describes the evolution of the probability density of some tracer particles on the doma

134 n equations for the time-dependent bivariate probability density of subsarcolemmal subspace and junct

135 ions are derived relating the time-dependent probability density of subsarcolemmal subspace and junct

136 stem of advection-reaction equations for the probability density of the domain [Ca(2+)] conditioned o

137 ses of hundreds of percent to >1,000% in the probability density of the largest accumulations that ha

138 vity is inherited and amplified by the joint probability density of the observable data and the proce

139 ep, we find a non-parametric estimate of the probability density of the points and cluster the points

140 e Talbot effect for mDfs exists and that the probability density of the transmitted mDfs waves throug

141 probability and the Laplace transform of the probability density of time that a macromolecule spends

142 uted, and involves inference of the (parent) probability density of unfolding times from the histogra

143 cue and a visual representation of the prior probability density over locations, which changed on eac

144 l to with the required k-vector matching and probability density overlap.

145 The probability density plots thus revealed that beta1-integ

146 Upon temperature increase, the probability density pools then flood a larger basin, aki

147 Also the complex form of the high probability density region in the SAS suggests a nonline

148 ets providing largely uneven coverage of the probability density space.

149 hase-shifts and amplitude modulations in the probability density that are most pronounced for waves a

150 h and the on/off switch via the steady-state probability density, the joint steady-state probability

151 te constants are determined from analysis of probability densities, which allows a much wider range o

152 MGMs consist of a parameterized joint probability density, which implies a network structure o