Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic Bayesian networks von HUSMEIER, Dirk

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Optimal estimation of drift and diffusion coefficients in the presence of static localization error von Devlin, J, Husmeier, D, Mackenzie, J A

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Detecting recombination in 4-taxa DNA sequence alignments with Bayesian hidden Markov models and Markov chain Monte Carlo von Husmeier, Dirk, McGuire, Gráinne

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Bayesian approaches to Gaussian mixture modeling von Roberts, S.J., Husmeier, D., Rezek, I., Penny, W.

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Detecting interspecific recombination with a pruned probabilistic divergence measure von Husmeier, Dirk, Wright, Frank, Milne, Iain

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TOPALi: software for automatic identification of recombinant sequences within DNA multiple alignments von Milne, Iain, Wright, Frank, Rowe, Glenn, Marshall, David F., Husmeier, Dirk, McGuire, Gràinne

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Discriminating between rate heterogeneity and interspecific recombination in DNA sequence alignments with phylogenetic factorial hidden Markov models von Husmeier, Dirk

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Learning non-stationary conditional probability distributions von Husmeier, D

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Detection of recombination in DNA multiple alignments with hidden Markov models von Husmeier, D, Wright, F

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Neural Networks for Predicting Conditional Probability Densities: Improved Training Scheme Combining EM and RVFL von Husmeier, Dirk, Taylor, John G.

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Issue Information von Boys, R. J., Stallard, N., Chen, Q., Deardon, R., Gaetan, C., Greven, S., Husmeier, D., Jermyn, I. H., Kalogeropoulos, K., Kneib, T., Lewin, A., Liverani, S., Marra, G., Mateu, J., McCrea, R., Oberski, D., Roy, J., Schildcrout, J., Shortreed, S., Wages, N. A.

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Applied Statistics von Boys, R. J., Stallard, N., Chen, Q., Deardon, R., Gaetan, C., Greven, S., Husmeier, D., Jermyn, I. H., Kalogeropoulos, K., Kneib, T., Lewin, A., Liverani, S., Marra, G., Mateu, J., Mccrea, R., Oberski, D., Roy, J., Schildcrout, J., Shortreed, S., Wages, N. A.

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An empirical evaluation of Bayesian sampling with hybrid Monte Carlo for training neural network classifiers von Husmeier, D., Penny, W.D., Roberts, S.J.

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Applied Statistics von Boys, R. J., Stallard, N., Chen, Q., Clarke, P., Deardon, R., Gaetan, C., Greven, S., Husmeier, D., Kalogeropoulos, K., Lewin, A., Liverani, S., Mateu, J., McCrea, R., Oberski, D., Ogden, T., Ranjan, P., Roy, J., Scheike, T., Schildcrout, J., Shortreed, S., Wages, N. A.

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The Bayesian Evidence Scheme for Regularizing Probability-Density Estimating Neural Networks von Husmeier, Dirk

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Segmenting bacterial and viral DNA sequence alignments with a trans-dimensional phylogenetic factorial hidden Markov model von Lehrach, Wolfgang P., Husmeier, Dirk

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Probabilistic divergence measures for detecting interspecies recombination von Husmeier, Dirk, Wright, Frank

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A regularized discriminative model for the prediction of protein–peptide interactions von Lehrach, Wolfgang P., Husmeier, Dirk, Williams, Christopher K. I.

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Reinforcement learning in a rule-based navigator for robotic manipulators von Althoefer, Kaspar, Krekelberg, Bart, Husmeier, Dirk, Seneviratne, Lakmal

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Detecting recombination with MCMC von Husmeier, Dirk, McGuire, Gráinne

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