Diego Pablo Ruiz Padillo

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We investigate in this paper reverse engineering of gene regulatory networks from time-series microarray data. We apply dynamic Bayesian networks (DBNs) for modeling cell cycle regulations. In developing a network inference algorithm, we focus on soft solutions that can provide a posteriori probability (APP) of network topology. In particular, we propose a(More)
We propose a method for blind multiuser detection (MUD) in synchronous systems over flat and fast Rayleigh fading channels. We adopt an autoregressive-moving-average (ARMA) process to model the temporal correlation of the channels. Based on the ARMA process, we propose a novel time-observation state-space model (TOSSM) that describes the dynamics of the(More)
In this work, we study the estimation of mixtures of symmetric alpha-stable distributions using Bayesian inference. We utilise numerical Bayesian sampling techniques such as Markov chain Monte Carlo (MCMC). Our estimation technique is capable of estimating also the number of alpha-stable components in the mixture in addition to the component parameters and(More)
We propose a Bayesian approach for constructing gene networks based on microarray data. Especially, we focus on Bayesian methods that can provide soft (probabilistic) information. This soft information is attractive not only for its ability to measure the level of confidence of the solution, but also because it can be used to realize Bayesian data(More)
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