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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 types, those with continuous and categorical random variables.
5 independent identically distributed (i.i.d.) random variables.
6 s and nonlinear dynamics of the ratio of two random variables.
7 of uniform representation of the continuous random variables.
8 of the maximum of multinomially distributed random variables.
9 ions identified in the structure analysis as random variables.
10 dent (resp., freely independent) n-tuples of random variables.
11 th all parameters of the model considered as random variables.
12 intercross case) independent standard normal random variables.
13 arameters by considering these parameters as random variables.
14 re thus treated as parameters rather than as random variables.
15 conditional expectation of a network-valued random variable across the values of a continuous predic
17 treats each effect of gene substitution as a random variable and directly estimates and tests the var
19 diploid combination of founder alleles as a random variable and only estimate the variance of these
20 oth the distributions of the true underlying random variable and the contaminating errors are normal.
21 ameters are all treated as jointly dependent random variables and sampled via Metropolis-Hastings Mar
23 is distributed as a weighted sum of Poisson random variables and we implement a saddlepoint approxim
24 fixation indices are parameters, rather than random variables, and these parameters are expressed in
26 conditional dependence structure of a set of random variables by a graph, has wide applications in th
27 r the means, variances and covariance of the random variables by the method of system-size expansion
29 namely that they are asymmetrically bounded random variables, constrained by a nonelastic boundary a
31 idth at baseline were no more effective than random variables for predicting OA progression (AUC 0.52
34 y of the density function of the independent random variables generating the sequence, asymptotically
35 the means, variances, and covariance of the random variables have been derived through the system-si
37 states that the variance V of a nonnegative random variable is a power function of its mean M; i.e.,
38 om clone to clone and may be thought of as a random variable; its probability distribution was estima
39 e hidden peak status by a set of independent random variables, leading to more tractable computation.
40 vailable data may reflect a true but unknown random variable of interest plus an additive error, whic
43 nti-portfolio effect emerges for products of random variables or equations involving multiplicative c
44 iate to treat the effect of each allele as a random variable so that a single variance rather than in
45 nodes and that of chosen existing nodes are random variables so that the size of each hyperedge is n
47 ber of populations have both been considered random variables that follow a Dirichlet process prior.
49 state reaction fluxes and transport rates as random variables we are able to propagate the uncertaint
50 probability of the range of sum of Markovian random variables, we propose formulae for approximating
51 analyte represents an independent Bernoulli random variable, which is then used to predict the binom
52 ndard framework of thermodynamics, work is a random variable whose average is bounded by the change i
54 tion nuisance parameters that are treated as random variables with known distributions, as opposed to
55 r each gene, mu is itself a zero-mean normal random variable [with a priori distribution N(0,tau 2)],
56 tify the strength of association between two random variables without bias for relationships of a spe
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