Modeling Conditional Probabilities with Committees of RVFL Networks. - Dirk Husmeier; John G. Taylor (ICANN, 1997)

Predicting Conditional Probability Densities with the Gaussian Mixture - RVFL Network. - Dirk Husmeier; John G. Taylor (ICANNGA, 1997)

Predicting Conditional Probability Densities of Stationary Stochastic Time Series. - Dirk Husmeier; John G. Taylor (Neural Networks, 1997)

Neural Networks for Predicting Conditional Probability Densities: Improved Training Scheme Combining EM and RVFL. - Dirk Husmeier; John G. Taylor (Neural Networks, 1998)

Bayesian Approaches to Gaussian Mixture Modeling. - Stephen J. Roberts; Dirk Husmeier; Iead Rezek; William D. Penny (IEEE Trans. Pattern Anal. Mach. Intell., 1998)

Neural networks for conditional probability estimation - forecasting beyond point predictions. - Dirk Husmeier (Springer, 1999)

Neural networks for predicting Kaposi's sarcoma. - Dirk Husmeier; Gillian S. Patton; Myra O. McClure; John R. W. Harris; Stephen J. Roberts (IJCNN, 1999)

An empirical evaluation of Bayesian sampling with hybrid Monte Carlo for training neural network classifiers. - Dirk Husmeier; William D. Penny; Stephen J. Roberts (Neural Networks, 1999)

The Bayesian Paradigm: Second Generation Neural Computing. - William D. Penny; Dirk Husmeier; Stephen J. Roberts (Artificial Neural Networks in Biomedicine, 2000)

Detecting Sporadic Recombination in DNA Alignments with Hidden Markov Models. - Dirk Husmeier; Frank Wright (German Conference on Bioinformatics, 2000)

The Bayesian Evidence Scheme for Regularizing Probability-Density Estimating Neural Networks. - Dirk Husmeier (Neural Comput., 2000)

Learning non-stationary conditional probability distributions. - Dirk Husmeier (Neural Networks, 2000)

Approximate Bayesian Discrimination between Alternative DNA Mosaic Structures. - Dirk Husmeier; Frank Wright (German Conference on Bioinformatics, 2001)

Probabilistic divergence measures for detecting interspecies recombination. - Dirk Husmeier; Frank Wright (ISMB (Supplement of Bioinformatics), 2001)

Reinforcement learning in a rule-based navigator for robotic manipulators. - Kaspar Althoefer; Bart Krekelberg; Dirk Husmeier; Lakmal D. Seneviratne (Neurocomputing, 2001)

Detecting recombination with MCMC. - Dirk Husmeier; Gráinne McGuire (ISMB, 2002)

A Bayesian approach to discriminate between alternative DNA sequence segmentations. - Dirk Husmeier; Frank Wright (Bioinform., 2002)

Detection of Recombination in DNA Multiple Alignments with Hidden Markov Models. - Dirk Husmeier; Frank Wright (J. Comput. Biol., 2002)

Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic Bayesian networks. - Dirk Husmeier (Bioinform., 2003)

TOPALi: software for automatic identification of recombinant sequences within DNA multiple alignments. - Iain Milne; Frank Wright; Glenn Rowe; David F. Marshall; Dirk Husmeier; Gráinne McGuire (Bioinform., 2004)

Discriminating between rate heterogeneity and interspecific recombination in DNA sequence alignments with phylogenetic factorial hidden Markov models. - Dirk Husmeier (ECCB/JBI, 2005)

Probabilistic in Silico Prediction of Protein-Peptide Interactions. - Wolfgang P. Lehrach; Dirk Husmeier; Christopher K. I. Williams (Systems Biology and Regulatory Genomics, 2005)

Detecting interspecific recombination with a pruned probabilistic divergence measure. - Dirk Husmeier; Frank Wright; Iain Milne (Bioinform., 2005)

A regularized discriminative model for the prediction of protein-peptide interactions. - Wolfgang P. Lehrach; Dirk Husmeier; Christopher K. I. Williams (Bioinform., 2006)

Comparative evaluation of reverse engineering gene regulatory networks with relevance networks, graphical gaussian models and bayesian networks. - Adriano Velasque Werhli; Marco Grzegorczyk; Dirk Husmeier (Bioinform., 2006)

Modelling non-stationary gene regulatory processes with a non-homogeneous Bayesian network and the allocation sampler. - Marco Grzegorczyk; Dirk Husmeier; Kieron D. Edwards; Peter Ghazal; Andrew J. Millar (Bioinform., 2008)

Gene Regulatory Network Reconstruction by Bayesian Integration of Prior Knowledge and/or Different Experimental Conditions. - Adriano Velasque Werhli; Dirk Husmeier (J. Bioinform. Comput. Biol., 2008)

Improving the structure MCMC sampler for Bayesian networks by introducing a new edge reversal move. - Marco Grzegorczyk; Dirk Husmeier (Mach. Learn., 2008)

Non-stationary continuous dynamic Bayesian networks. - Marco Grzegorczyk; Dirk Husmeier (NIPS, 2009)

Avoiding Spurious Feedback Loops in the Reconstruction of Gene Regulatory Networks with Dynamic Bayesian Networks. - Marco Grzegorczyk; Dirk Husmeier (PRIB, 2009)

Distinguishing Regional from Within-Codon Rate Heterogeneity in DNA Sequence Alignments. - Alexander V. Mantzaris; Dirk Husmeier (PRIB, 2009)

TOPALi v2: a rich graphical interface for evolutionary analyses of multiple alignments on HPC clusters and multi-core desktops. - Iain Milne; Dominik Lindner; Micha Bayer; Dirk Husmeier; Gráinne McGuire; David F. Marshall; Frank Wright (Bioinform., 2009)

Modelling Transcriptional Regulation with a Mixture of Factor Analyzers and Variational Bayesian Expectation Maximization. - Kuang Lin; Dirk Husmeier (EURASIP J. Bioinform. Syst. Biol., 2009)

Mixtures of Factor Analyzers for Modeling Transcriptional Regulation. - Kuang Lin; Dirk Husmeier (Learning and Inference in Computational Systems Biology, 2010)

Heterogeneous Continuous Dynamic Bayesian Networks with Flexible Structure and Inter-Time Segment Information Sharing. - Frank Dondelinger; Sophie Lèbre; Dirk Husmeier (ICML, 2010)

Inter-time segment information sharing for non-homogeneous dynamic Bayesian networks. - Dirk Husmeier; Frank Dondelinger; Sophie Lèbre (NIPS, 2010)

Modelling Nonstationary Gene Regulatory Processes. - Marco Grzegorczyk; Dirk Husmeier; Jörg Rahnenführer (Adv. Bioinformatics, 2010)

Inferring species interaction networks from species abundance data: A comparative evaluation of various statistical and machine learning methods. - Ali Faisal; Frank Dondelinger; Dirk Husmeier; Colin M. Beale (Ecol. Informatics, 2010)

Improvements in the reconstruction of time-varying gene regulatory networks: dynamic programming and regularization by information sharing among genes. - Marco Grzegorczyk; Dirk Husmeier (Bioinform., 2011)

Modelling non-stationary dynamic gene regulatory processes with the BGM model. - Marco Grzegorczyk; Dirk Husmeier; Jörg Rahnenführer (Comput. Stat., 2011)

Non-homogeneous dynamic Bayesian networks for continuous data. - Marco Grzegorczyk; Dirk Husmeier (Mach. Learn., 2011)

Hierarchical Bayesian models in ecology: Reconstructing species interaction networks from non-homogeneous species abundance data. - Andrej Aderhold; Dirk Husmeier; Jack J. Lennon; Colin M. Beale; V. Anne Smith (Ecol. Informatics, 2012)

Bayesian regularization of non-homogeneous dynamic Bayesian networks by globally coupling interaction parameters. - Marco Grzegorczyk; Dirk Husmeier (AISTATS, 2012)

Reconstructing ecological networks with hierarchical Bayesian regression and Mondrian processes. - Andrej Aderhold; Dirk Husmeier; V. Anne Smith (AISTATS, 2013)

ODE parameter inference using adaptive gradient matching with Gaussian processes. - Frank Dondelinger; Dirk Husmeier; Simon Rogers; Maurizio Filippone (AISTATS, 2013)

A Bayesian approach for parameter estimation in the extended clock gene circuit of Arabidopsis thaliana. - Catherine F. Higham; Dirk Husmeier (BMC Bioinform., 2013)

Non-homogeneous dynamic Bayesian networks with Bayesian regularization for inferring gene regulatory networks with gradually time-varying structure. - Frank Dondelinger; Sophie Lèbre; Dirk Husmeier (Mach. Learn., 2013)

Regularization of non-homogeneous dynamic Bayesian networks with global information-coupling based on hierarchical Bayesian models. - Marco Grzegorczyk; Dirk Husmeier (Mach. Learn., 2013)

Sparse Bayesian Variable Selection for the Identification of Antigenic Variability in the Foot-and-Mouth Disease Virus. - Vinny Davies; Richard E. Reeve; William T. Harvey; Francois F. Maree; Dirk Husmeier (AISTATS, 2014)

Inference of Circadian Regulatory Networks. - Marco Grzegorczyk; Andrej Aderhold; V. Anne Smith; Dirk Husmeier (IWBBIO, 2014)

Selecting Random Effect Components in a Sparse Hierarchical Bayesian Model for Identifying Antigenic Variability. - Vinny Davies; Richard E. Reeve; William T. Harvey; Dirk Husmeier (CIBB, 2015)

Controversy in mechanistic modelling with Gaussian processes. - Benn Macdonald; Catherine F. Higham; Dirk Husmeier (ICML, 2015)

Inference of Circadian Regulatory Pathways Based on Delay Differential Equations. - Catherine F. Higham; Dirk Husmeier (IWBBIO (2), 2015)

Computational Inference in Systems Biology. - Benn Macdonald; Dirk Husmeier (IWBBIO (2), 2015)

Parameter Inference in Differential Equation Models of Biopathways Using Time Warped Gradient Matching. - Mu Niu; Simon Rogers; Maurizio Filippone; Dirk Husmeier (CIBB, 2016)

Inference in a Partial Differential Equations Model of Pulmonary Arterial and Venous Blood Circulation Using Statistical Emulation. - Umberto Noè; Weiwei Chen; Maurizio Filippone; Nicholas Hill; Dirk Husmeier (CIBB, 2016)

Fast Parameter Inference in Nonlinear Dynamical Systems using Iterative Gradient Matching. - Mu Niu; Simon Rogers; Maurizio Filippone; Dirk Husmeier (ICML, 2016)

A sparse hierarchical Bayesian model for detecting relevant antigenic sites in virus evolution. - Vinny Davies; Richard E. Reeve; William T. Harvey; Francois F. Maree; Dirk Husmeier (Comput. Stat., 2017)

Targeting Bayes factors with direct-path non-equilibrium thermodynamic integration. - Marco Grzegorczyk; Andrej Aderhold; Dirk Husmeier (Comput. Stat., 2017)

Approximate Bayesian inference in semi-mechanistic models. - Andrej Aderhold; Dirk Husmeier; Marco Grzegorczyk (Stat. Comput., 2017)

Multiphase MCMC Sampling for Parameter Inference in Nonlinear Ordinary Differential Equations. - Alan Lazarus; Dirk Husmeier; Theodore Papamarkou (AISTATS, 2018)

ShinyKGode: an interactive application for ODE parameter inference using gradient matching. - Joe Wandy; Mu Niu; Diana Giurghita; Rónán Daly; Simon Rogers; Dirk Husmeier (Bioinform., 2018)

On a New Improvement-Based Acquisition Function for Bayesian Optimization. - Umberto Noè; Dirk Husmeier (CoRR, 2018)

Statistical inference in mechanistic models: time warping for improved gradient matching. - Mu Niu; Benn Macdonald; Simon Rogers; Maurizio Filippone; Dirk Husmeier (Comput. Stat., 2018)

Model selection via marginal likelihood estimation by combining thermodynamic integration and gradient matching. - Benn Macdonald; Dirk Husmeier (Stat. Comput., 2019)

Neural network-based left ventricle geometry prediction from CMR images with application in biomechanics. - Lukasz Romaszko; Agnieszka Borowska; Alan Lazarus; David Dalton; Colin Berry; Xiaoyu Luo; Dirk Husmeier; Hao Gao 002 (Artif. Intell. Medicine, 2021)

R package for statistical inference in dynamical systems using kernel based gradient matching: KGode. - Mu Niu; Joe Wandy; Rónán Daly; Simon Rogers; Dirk Husmeier (Comput. Stat., 2021)

Gaussian process enhanced semi-automatic approximate Bayesian computation: parameter inference in a stochastic differential equation system for chemotaxis. - Agnieszka Borowska; Diana Giurghita; Dirk Husmeier (J. Comput. Phys., 2021)

Temporal extrapolation of heart wall segmentation in cardiac magnetic resonance images via pixel tracking. - Arash Rabbani; Hao Gao 002; Dirk Husmeier (CoRR, 2022)

Transferable species distribution modelling: Comparative performance of Generalised Functional Response models. - Shaykhah Aldossari; Dirk Husmeier; Jason Matthiopoulos (Ecol. Informatics, 2022)

Emulation-accelerated Hamiltonian Monte Carlo algorithms for parameter estimation and uncertainty quantification in differential equation models. - Luiza Mihaela Paun; Dirk Husmeier (Stat. Comput., 2022)

The Fully Convolutional Transformer for Medical Image Segmentation. - Athanasios Tragakis; Chaitanya Kaul; Roderick Murray-Smith; Dirk Husmeier (WACV, 2023)

A Bayesian approach to incorporate structural data into the mapping of genotype to antigenic phenotype of influenza A(H3N2) viruses. - William T. Harvey; Vinny Davies; Rodney S. Daniels; Lynne Whittaker; Victoria Gregory; Alan J. Hay; Dirk Husmeier; John W. McCauley; Richard E. Reeve (PLoS Comput. Biol., 2023)

Stochastic variational inference for scalable non-stationary Gaussian process regression. - Ionut Paun; Dirk Husmeier; Colin J. Torney (Stat. Comput., 2023)

Image-based estimation of the left ventricular cavity volume using deep learning and Gaussian process with cardio-mechanical applications. - Arash Rabbani; Hao Gao 002; Alan Lazarus; David Dalton; Yuzhang Ge; Kenneth Mangion; Colin Berry; Dirk Husmeier (Comput. Medical Imaging Graph., 2023)

Physics and Lie symmetry informed Gaussian processes. - David Dalton; Dirk Husmeier; Hao Gao 002 (ICML, 2024)

Automatic detection of myocardial ischaemia using generalisable spatio-temporal hierarchical Bayesian modelling of DCE-MRI. - Yalei Yang; Dirk Husmeier; Hao Gao 002; Colin Berry; David Carrick; Aleksandra Radjenovic (Comput. Medical Imaging Graph., 2024)

Bayesian Inversion of Frequency-Domain Airborne EM Data With Spatial Correlation Prior Information. - Jianmei Zhou; Dirk Husmeier; Hao Gao 002; Changchun Yin; Changkai Qiu; Xu Jing; Yanfu Qi; Wentao Liu (IEEE Trans. Geosci. Remote. Sens., 2024)

Advanced statistical inference of myocardial stiffness: A time series Gaussian process approach of emulating cardiac mechanics for real-time clinical decision support. - Yuzhang Ge; Dirk Husmeier; Arash Rabbani; Hao Gao 002 (Comput. Biol. Medicine, 2025)

Approximate Bayesian inference in a model for self-generated gradient collective cell movement. - Jon Devlin; Agnieszka Borowska; Dirk Husmeier; John A. Mackenzie (Comput. Stat., 2025)