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- Ka Yee Yeung, Chris Fraley, A. Murua, Adrian E. Raftery, Walter L. Ruzzo
- Bioinformatics
- 2001

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)

- Raphael Gottardo, Julian Besag, Matthew Stephens, Alejandro Murua
- Biostatistics
- 2006

We describe a probabilistic approach to simultaneous image segmentation and intensity estimation for complementary DNA microarray experiments. The approach overcomes several limitations of existing methods. In particular, it (a) uses a flexible Markov random field approach to segmentation that allows for a wider range of spot shapes than existing methods,… (More)

- A. Murua
- Computing
- 1997

A class of half-explicit methods for index 2 differential-algebraic systems in Hessenberg form is proposed, which takes advantage of the partitioned structure of such problems. For this family of methods, which we call partitioned half-explicit Runge-Kutta methods, a better choice in the parameters of the method than for previously available half-explicit… (More)

- T. Richardson, S. Roy, R. A. Ali, A. Murua
- 2001

This work concerns sequential techniques for the canonical blind deconvolution problem in communications signal processing, relating to the estimation of the transmitted (discrete-valued) data sequence from the observed signal at the receiver input, in the presence of unknown linear channel filtering, without recourse to extended training sequences for… (More)

- Ka Yee Yeung, Chris Fraley, Alejandro Murua, Adrian E. Rafter, Walter L. Ruzzo
- 2001

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)

- R. A. Ali, A. Murua, T. Richardson, S. Roy
- 2002 11th European Signal Processing Conference
- 2002

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)

- Alejandro Murua
- 1999

A practical and useful notion of weak dependence between many classifiers constructed with the same training data is introduced. It is shown that when (a) this weak dependence is rather low, and (b) the expected margins are large, exponential bounds on the true error rates can be achieved. Empirical results with randomized trees, and trees constructed via… (More)

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