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1 pring is unknown and can be represented as a random variable.
2 rarchy, and treat the population number as a random variable.
3 stitution is modified by a gamma-distributed random variable.
4 directed random graphs, this observable is a random variable.
5  population is constructed as a hierarchical random variable.
6 ior defined as independent realizations of a random variable.
7  desired marginal expectation for the binary random variable.
8 arameters by considering these parameters as random variables.
9 types, those with continuous and categorical random variables.
10 independent identically distributed (i.i.d.) random variables.
11 s and nonlinear dynamics of the ratio of two random variables.
12  of uniform representation of the continuous random variables.
13  of the maximum of multinomially distributed random variables.
14 ions identified in the structure analysis as random variables.
15 dent (resp., freely independent) n-tuples of random variables.
16 th all parameters of the model considered as random variables.
17 intercross case) independent standard normal random variables.
18 re thus treated as parameters rather than as random variables.
19 he joint fluctuations of spatially dependent random variables.
20 ly implement to compute dependencies between random variables.
21 t functional dependency between two discrete random variables.
22 ely model interactions between the Bernoulli random variables.
23 es of health system access and referral) are random variables.
24 r and various nonlinear dependencies between random variables.
25 ng time period and program identification as random variables, a multivariable mixed Cox proportional
26  conditional expectation of a network-valued random variable across the values of a continuous predic
27  datasets today contain tens of thousands of random variables across millions of samples (for example
28 ive approach that treats the genotype as the random variable and conditions upon the phenotype.
29 treats each effect of gene substitution as a random variable and directly estimates and tests the var
30                                 q-value is a random variable and it may underestimate FDR in practice
31 is to treat the temperature as a fluctuating random variable and not a control parameter as is usuall
32  diploid combination of founder alleles as a random variable and only estimate the variance of these
33 oth the distributions of the true underlying random variable and the contaminating errors are normal.
34 times from these technologies are themselves random variables and analysis of these data, therefore,
35 ble were calculated as the mean of Bernoulli random variables and for the risk estimate, by the delta
36  the perspective of statistical whitening of random variables and propose a simple yet flexible proba
37 ameters are all treated as jointly dependent random variables and sampled via Metropolis-Hastings Mar
38          Extensions of the model to discrete random variables and to nonlinear relationships between
39  is distributed as a weighted sum of Poisson random variables and we implement a saddlepoint approxim
40 t marker intensities are viewed as dependent random variables, and the mutual information (MI) betwee
41 fixation indices are parameters, rather than random variables, and these parameters are expressed in
42        The problem in predicting the unknown random variable arises in many applied situations where
43 conditional dependence structure of a set of random variables by a graph, has wide applications in th
44 r the means, variances and covariance of the random variables by the method of system-size expansion
45  convergence (within a given tolerance) is a random variable, called the halting time.
46 g key parameters in the algal bloom model as random variables changes the timing, intensity and overa
47  namely that they are asymmetrically bounded random variables, constrained by a nonelastic boundary a
48  a tumor growing initially, the FPTD for the random variables describing the first time that the grow
49 fixed for an interval of time until a binary random variable determines a switch.
50 robabilistically, viewing flow properties as random variables distributed according to joint probabil
51                Here we framed the problem as random variable estimation problem and studied the conve
52 idth at baseline were no more effective than random variables for predicting OA progression (AUC 0.52
53 known when, in fact, they are unknown (i.e., random variables from some distribution).
54 of the density function f of the independent random variables generating the process.
55 y of the density function of the independent random variables generating the sequence, asymptotically
56                Local authority district as a random variable had a strong but variable effect for cla
57  the means, variances, and covariance of the random variables have been derived through the system-si
58 pendent and identically distributed (i.i.d.) random variables, i.e., a Bernoulli sequence.
59 nerated with normally distributed univariate random variables in T.
60  states that the variance V of a nonnegative random variable is a power function of its mean M; i.e.,
61 om clone to clone and may be thought of as a random variable; its probability distribution was estima
62 e hidden peak status by a set of independent random variables, leading to more tractable computation.
63 vailable data may reflect a true but unknown random variable of interest plus an additive error, whic
64 mpling process from a big source, which is a random variable of size at least a few gigabytes.
65  probability distributions of the elementary random variables of the process.
66 patient's experience of care as a continuous random variable on the open interval ([Formula: see text
67 nti-portfolio effect emerges for products of random variables or equations involving multiplicative c
68                                            A random variable (rv) was generated by randomly sampling
69        We model neuronal receptive fields as random, variable samples from parameterized distribution
70                                 The behavior random variable selects from a distribution of six known
71 iate to treat the effect of each allele as a random variable so that a single variance rather than in
72  nodes and that of chosen existing nodes are random variables so that the size of each hyperedge is n
73                                Modeling with random variables suggests that similar results would be
74 ime models, where the host reproduction is a random variable that varies from year to year and drives
75 ber of populations have both been considered random variables that follow a Dirichlet process prior.
76      Mixed Markov models contain node-valued random variables that, when instantiated, augment the gr
77 nputs into k qubits and representing data as random variables to seamlessly connect layers without me
78 arvesting as indicator variable, and site as random variable (two plots nested to same precipitation)
79 using a logistic model and the hospital as a random variable was performed to identify independent pr
80  year of admission and the source dataset as random variables was used to identify risk factors for d
81 state reaction fluxes and transport rates as random variables we are able to propagate the uncertaint
82 probability of the range of sum of Markovian random variables, we propose formulae for approximating
83  primary case is modeled as a binomial(m, p) random variable where p is the SAR.
84  analyte represents an independent Bernoulli random variable, which is then used to predict the binom
85  the assumption that the genetic effects are random variables, which is opposite to the fixed effect
86 ndard framework of thermodynamics, work is a random variable whose average is bounded by the change i
87                                     A scalar random variable whose deterministic limit rrho(kappa) ca
88 ationship is not a constant; rather, it is a random variable whose distribution depends on cell size
89 der methods for generating draws of a binary random variable whose expectation conditional on covaria
90 contact counts between two loci as a Poisson random variable whose intensity is a decreasing function
91  The survival time is approximately a normal random variable with simple formulas for its mean and va
92 orated into models by treating parameters as random variables with distributions, rather than fixed q
93 tion nuisance parameters that are treated as random variables with known distributions, as opposed to
94 nknown rate and changepoints are modelled as random variables with known prior distributions.
95 r each gene, mu is itself a zero-mean normal random variable [with a priori distribution N(0,tau 2)],
96 tify the strength of association between two random variables without bias for relationships of a spe