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1 likelihood of the growth rate and leads to a maximum-likelihood estimate.
2 ethod relies on random effects modeling with maximum likelihood estimates.
3 od estimator of and numerical computation of maximum likelihood estimates.
4 sian methods performed better as compared to maximum likelihood estimates.
5                           Excluding petites, maximum-likelihood estimates adjusted for the effect of
6 ubsampling bootstrap procedure to obtain the maximum likelihood estimates and confidence bounds for c
7 evelop the asymptotic variance-covariance of maximum likelihood estimates and evaluate the accuracy o
8  the new Bayesian framework and the previous maximum likelihood estimate approach, showing that the B
9 sinterpretation of results, as compared to a maximum likelihood estimate based approach.
10                                              Maximum likelihood estimates conducted by multilocus lin
11 l cells were described by a model based on a maximum likelihood estimate for cellular damage, repair
12 , platform-independent program that computes maximum likelihood estimates for finite-mixture models,
13 seven diverse eukaryotic genomes and provide maximum likelihood estimates for rates and numbers of in
14                                              Maximum likelihood estimates for the duration of untreat
15  read counts are low or highly variable, the maximum likelihood estimates for the LFCs has high varia
16 timization algorithm to find the constrained maximum likelihood estimates for the scGTM parameters.
17 Here, we develop a novel method that derives maximum likelihood estimates for the strength of direct
18 ect the rate of primary infection by using a maximum-likelihood estimate for a simpler model with no
19 e demonstrate this point by showing that the maximum-likelihood estimate for L produced by the method
20 ed expectation maximization to obtain robust maximum-likelihood estimates for insertion, deletion and
21 ndel rate, and the evolutionary time are all maximum likelihood estimated from the sequences being al
22                                          Our maximum-likelihood estimate indicates a rate of recombin
23 imate ages under the intermediate model, the maximum likelihood estimate is significantly less inflat
24 d zero theta values between populations, the maximum likelihood estimate is the same as that given by
25                              SEM models with maximum-likelihood estimates made use of data from 887 p
26  the adverse events studied are rare and the maximum likelihood estimates may be biased.
27 ther independent objective function based on maximum likelihood estimates (MLE) can be used to align
28 , it gives an efficient way of obtaining the maximum-likelihood estimate (MLE) for a given tree topol
29                        We prove that (i) the maximum-likelihood estimate (MLE) is biased, (ii) the va
30                             The hierarchical maximum-likelihood estimate (MLE) is shown to be a more
31 etic markers, and so the distribution of the maximum-likelihood estimate (MLE) of QTL location has th
32 -locus sample configuration to have a finite maximum-likelihood estimate (MLE) of rho.
33                          GCHap quickly finds maximum likelihood estimates (MLEs) of frequencies of ha
34  the likelihood-ratio test statistic and the maximum-likelihood estimates (MLEs) of parameters includ
35                                          The maximum-likelihood estimates (MLEs) of the logistic regr
36                                              Maximum-likelihood estimates (MLEs) of the new model rev
37 cle we explore statistical properties of the maximum-likelihood estimates (MLEs) of the selection and
38                           Here, we derived a maximum-likelihood estimate model that converts frequent
39 ned with all estimations available gives the maximum likelihood estimate of glacial cooling as -5.85
40 th, are diagnostic metrics of SLS: i.e., the maximum likelihood estimate of the area under the receiv
41                                          The maximum likelihood estimate of the characteristic lifeti
42 vidual genotype or sequence data to obtain a maximum likelihood estimate of the global admixture prop
43                       The program provides a maximum likelihood estimate of the rate and also the ass
44 tool for the program STELLS, which finds the maximum likelihood estimate of the species tree from the
45  as sample SNPs leads to large errors in the maximum likelihood estimate of Theta.
46 o extend the gene counting method to compute maximum likelihood estimates of allele frequencies for s
47            Finite sample bias may occur when maximum likelihood estimates of associations are obtaine
48 n-maximization (EM) algorithm for generating maximum likelihood estimates of gametic frequencies from
49 ative model for sequencing is used to obtain maximum likelihood estimates of gaps between contigs and
50 n intraclass correlations (ICCs) and using a maximum likelihood estimates of genetic variance were ca
51            We develop a method for obtaining maximum likelihood estimates of haplotype frequencies fo
52 onsider the problem of interpreting negative maximum likelihood estimates of heritability that someti
53 n all pedigrees by nonparametric methods and maximum likelihood estimates of identity by descent shar
54                                              Maximum likelihood estimates of model parameters are obt
55                                        Using maximum likelihood estimates of nonsynonymous/synonymous
56 regression procedures were used to calculate maximum likelihood estimates of odds ratios and 95% conf
57 arge datasets since they involve calculating maximum likelihood estimates of pairwise evolutionary di
58 ew software tool, RelateAdmix, for obtaining maximum likelihood estimates of pairwise relatedness fro
59 r, this model produced increasingly variable maximum likelihood estimates of parameters as prevalence
60 Our applications are new and include finding maximum likelihood estimates of parameters in a single c
61                Algorithmic details to obtain maximum likelihood estimates of parameters on a large ph
62                                  We obtained maximum likelihood estimates of the common national effe
63    Algorithms are presented that provide the maximum likelihood estimates of the distribution's param
64 d using the neighbor joining method based on maximum likelihood estimates of the evolutionary distanc
65 ully sequenced eukaryotic genomes to provide maximum likelihood estimates of the number of introns pr
66        Our simulations show that ADMIXTURE's maximum likelihood estimates of the underlying admixture
67 moother technique iteratively calculates the maximum-likelihood estimate of Greenland ice sheet (GIS)
68                                            A maximum-likelihood estimate of the age of the allele is
69              We apply the method to obtain a maximum-likelihood estimate of the rate of recombination
70                                          The maximum-likelihood estimate of the ti/tv ratio changes w
71 ates based on random topologies, whereas the maximum-likelihood estimate of Ts/Tv based on the well-c
72          The computational methods generated maximum-likelihood estimates of allele-level haplotypes.
73 to investigate the statistical properties of maximum-likelihood estimates of demographic parameters.
74                                              Maximum-likelihood estimates of nonsynonymous substituti
75 e we study the statistical properties of the maximum-likelihood estimates of omega and d in pairwise
76 ort blocks gives an efficient way to compute maximum-likelihood estimates of parameters.
77                                 We find that maximum-likelihood estimates of parametric statistics sh
78 ectation maximization algorithm to calculate maximum-likelihood estimates of sharing (subject to user
79 expected Markov counting (EMC) that produces maximum-likelihood estimates of substitution counts for
80 provided analytical tools for predicting the maximum-likelihood estimates of the model parameters.
81 ic EM algorithm is implemented to obtain the maximum-likelihood estimates of the Poisson parameters t
82 ber of nonrecombining blocks, we can compute maximum-likelihood estimates of the time and strength of
83 w this problem can be corrected by obtaining maximum-likelihood estimates of the true allele frequenc
84 on test is described for situations in which maximum-likelihood estimates of the variance components
85         Numerical methods are used to derive maximum-likelihood estimates of the variance components,
86 mate-driven transmission model with the MLE (Maximum Likelihood Estimates) of the parameters retrospe
87  calculating confidence intervals around the maximum likelihood estimate, our model can both provide
88 iants of the method with each other and with maximum-likelihood estimates provided by the SAGE comput
89                                         AUC (maximum likelihood estimate +/- standard error) values i
90                                              Maximum likelihood estimates suggest that selection inte
91 ture models used robust and full information maximum likelihood estimates to account for missing data
92 ence of the condition and some properties of maximum likelihood estimates to obtain an expression for
93 anscripts to which they are mapped and finds maximum likelihood estimates using a joint Poisson model
94                                    To obtain maximum likelihood estimates, we developed a nested EM a
95 family-based Markov model was developed, and maximum likelihood estimates were produced for model par
96                                              Maximum likelihood estimates were used to assess minimum