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1 lgorithm for Gaussian mixed linear model via Gibbs sampling.
2 sampled from the posterior distribution with Gibbs sampling.
3 ian Markov chain Monte Carlo simulation with Gibbs sampling.
4 or Bayesian inference using Forward-Backward Gibbs sampling.
5 g the algorithmic update-order-invariance of Gibbs sampling.
6 hod called lambda-dynamics with bias-updated Gibbs sampling.
7 le maintaining highly parallelized chromatic Gibbs sampling.
8  to the state-of-the-art, including standard Gibbs sampling.
9 ine factor binding sites by using a Bayesian Gibbs sampling algorithm and an extensive protein locali
10                                 We develop a Gibbs sampling algorithm comprising partial reversible-j
11                                AlignACE is a Gibbs sampling algorithm for identifying motifs that are
12                           We use a collapsed Gibbs sampling algorithm for inference.
13 sitions, and incorporate these priors into a Gibbs sampling algorithm for motif discovery.
14                            Metropolis within Gibbs sampling algorithm is used to simulate from the po
15 hain Monte Carlo algorithm that combines the Gibbs sampling algorithm of HapSeq and Metropolis-Hastin
16                                            A Gibbs sampling algorithm then locates putative cis-actin
17  coefficients are dealt with by developing a Gibbs sampling algorithm to stochastically search throug
18 xt, Hierarchical Bayesian Modeling using the Gibbs Sampling algorithm was applied to identify the seg
19                                       Then a Gibbs sampling algorithm was applied to search for share
20                            It uses a blocked Gibbs sampling algorithm, which has a theoretical advant
21 y cross-species comparison using an extended Gibbs sampling algorithm.
22 single-letter codes and then uses a modified Gibbs-sampling algorithm to define the position specific
23  tools are good at detecting strong signals, Gibbs sampling algorithms give inconsistent results when
24 ption factor binding sites than the standard Gibbs sampling algorithms.
25 plied different statistical algorithms (both Gibbs sampling and expectation-maximization) in reconstr
26 ntal framework, we employed a combination of Gibbs sampling and linear regression to build a classifi
27 e Carlo sampling and, in particular, discuss Gibbs sampling and Metropolis random walk algorithms wit
28 portant factors governing the performance of Gibbs sampling and reversible jump for mapping multiple
29  deriving error bars for breakpoints using a Gibbs sampling approach.
30 e Carlo algorithm should be more robust than Gibbs sampling approaches to multimodality problems.
31                          We have developed a Gibbs sampling-based algorithm for the genomic mapping o
32                          We have developed a Gibbs sampling-based Bayesian motif clustering (BMC) alg
33 sequence motif was identified in all ACPs by Gibbs sampling-based computational analyses.
34 latory sequence motifs: (i) BioProspector, a Gibbs-sampling-based program for predicting regulatory m
35 otif of interest, masking DNA repeats during Gibbs sampling becomes unnecessary.
36                                     Applying Gibbs sampling, BICORN iteratively estimates model param
37 ithm called CompareProspector, which extends Gibbs sampling by biasing the search in regions conserve
38 and Markov chain Monte Carlo simulation with Gibbs sampling, calculating pooled odds ratios and assoc
39 of Bayesian variable selection methods using Gibbs sampling can be applied to the composite model spa
40 onzero temperature, the mixing time of local Gibbs sampling diverges in the thermodynamic limit.
41 hen combine the model sampling step with the Gibbs sampling framework for de novo motif discoveries.
42                                              Gibbs sampling, hidden Markov models, and other analysis
43               These motifs are refined using Gibbs sampling in competition with a null motif.
44 of the model using Markov Chain Monte Carlo, Gibbs sampling in particular, to simulate parameters fro
45                  In this paper, we develop a Gibbs-sampling-induced stochastic search procedure to ra
46                   Combining subsampling with Gibbs sampling is an interesting ensemble algorithm.
47                                         Then Gibbs sampling is repeated, allowing for frameshifts of
48 sterior distributions are in closed form, so Gibbs sampling is straightforward.
49                                              Gibbs sampling is used to assign rate categories (backgr
50 es; however, given that BayesPrism relies on Gibbs sampling, it is orders of magnitude more computati
51                                 Just Another Gibbs Sampling (JAGS) is a convenient tool to draw poste
52                               We developed a Gibbs sampling Markov chain Monte Carlo algorithm that p
53 ethods that could perform the same task, the Gibbs sampling method developed here exceeds their abili
54                                            A Gibbs sampling method was then developed to estimate the
55                                    Using the Gibbs sampling method, we also detected enrichment of NF
56 d has already been applied in the context of Gibbs sampling methods.
57 els of coding and non-coding regions and the Gibbs sampling multiple alignment program.
58                                         Slow Gibbs sampling of such codes enables fault-tolerant pass
59         Most approaches have utilized either Gibbs sampling or greedy strategies to identify such ele
60 MS then performs bicluster mining based on a Gibbs sampling paradigm.
61  biclustering model (BBC), and implemented a Gibbs sampling procedure for its statistical inference.
62                                          The Gibbs sampling procedure we use simultaneously maps ambi
63                                     Usually, Gibbs sampling requires a preliminary masking step, to a
64 ed framework by replacing the time-consuming Gibbs sampling step with a fixed-point algorithm.
65                        The method utilizes a Gibbs sampling strategy to model the cooperativity betwe
66                     Here we have developed a Gibbs sampling technique to identify genes whose express
67      Upon exploring variants of the standard Gibbs sampling technique to optimize the alignment, we d
68                                      We used Gibbs sampling to define a CRP(Mt) DNA motif that resemb
69 ge disequilibrium structure using paralleled Gibbs sampling to enhance statistical power.
70 er, it used Bayesian hierarchical model with Gibbs sampling to incorporate binding signals of these r
71 l parameters are estimated iteratively using Gibbs sampling to infer the joint posterior distribution
72                                QUILT employs Gibbs sampling to partition reads into maternal and pate
73                For example, GibbsDNA applies Gibbs sampling to random seeds, and MEME applies the EM
74                                              Gibbs sampling was used for Bayesian model inference, wi
75  numbers and the convergence efficiencies of Gibbs sampling were calculated and discussed for achievi
76       We present a novel algorithm, based on Gibbs sampling, which locates, de novo, the cis features