… As increasing amounts of array CGH data become available, there is a growing need for …
in the data. We adopt a Bayesian approach, relying on the hidden Markov model to account for …

… Section 7.5 uses simulation studies to compare our Bayesian HMM with alternative … a
Bayesian hierarchical approach for analyzing array CGH data based on a hidden Markov model. …

… As increasing amounts of array CGH data become available, there is a growing need for …
in the data. We adopt a Bayesian approach, relying on the hidden Markov model to account for …

… continuous-index hidden Markov model for aCGH data as well … Bayesian approach, assigning
prior distributions to all model … continuous-index hidden Markov model (HMM) approach to …

… to hidden Markov models, the hidden semi-Markov models allow the … , hidden semi-Markov
models are good at modeling … In addition, the Bayesian HMM [12] has many false positives. …

… a Bayesian hierarchical model, nonetheless we fit a hidden Markov model to the data
directly, therefore it has less unknown variables and decreases the risk of model overfitting. …

… In this section, the Arabidopsis Array-CGH data set is … for model evaluation identified in
resequencing data are … Array-CGH profiles using the Bayesian Baum-Welch algorithm. Next, each …

… , array based competitive genomic hybridization (array-CGH) has become available as a
powerful approach for genome-wide detection of DNA copy number changes. Array-CGH …

… Experimental results have demonstrated that CRF-CNV outperforms a Bayesian Hidden
Markov Model-based approach on both datasets in terms of copy number assignments. Com…

… We apply a hidden Markov model (HMM) to accurately identify CNAs from aCGH data. We
… Since our goal is to infer the CNA state from the data, we can make use of Bayes’ rule and …