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MOTIVATION Clustering is a useful exploratory technique for the analysis of gene expression data. Many different heuristic clustering algorithms have been proposed in this context. Clustering algorithms based on probability models offer a principled alternative to heuristic algorithms. In particular, model-based clustering assumes that the data is generated(More)
The paper considers non-autonomous oscillatory systems of ordinary differential equations with d ≥ 1 non-resonant constant frequencies. Formal series like those used nowadays to analyze the properties of numerical integrators are employed to construct higher-order averaged systems and the required changes of variables. With the new approach, the averaged(More)
  • A. Murua
  • 2006
We present an approach that allows performing computations related to the Baker-Campbell-Haussdorff (BCH) formula and its generalizations in an arbitrary Hall basis, using labeled rooted trees. In particular, we provide explicit formulas (given in terms of the structure of certain labeled rooted trees) of the continuous BCH formula. We develop a rewriting(More)
Before applying model-based clustering to gene expression data, we assessed the extent to which the Gaussian mixture assumption holds. Since we do not expect raw expression data to satisfy the Gaussian mixture assumption, we explored the degree of normality of each class after applying different data transformations. In particular, we studied two types of(More)
Sequential techniques for the canonical blind deconvolution problem have attracted the attention of computational Bayesians such as Liu and Chen (1995) who applied Sequential Importance Sampling (SIS) to this problem. Subsequently, several extensions have been proposed (e.g. Rejuvenation, Rejection Control, Fixed-Lag Smoothing, Metropolis-Hastings(More)
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