Wandy, J. , Niu, M., Giurghita, D., Daly, R. , Rogers, S. and Husmeier, D. (2018) ShinyKGode: an interactive application for ODE parameter inference using gradient matching. Bioinformatics, 34(13), pp. 2314-2315. (doi: 10.1093/bioinformatics/bty089) (PMID:29490021) (PMCID:PMC6022662)

Niu, M., Macdonald, B. , Rogers, S. , Filippone, M. and Husmeier, D. (2018) Statistical inference in mechanistic models: time warping for improved gradient matching. Computational Statistics, 33(2), pp. 1091-1123. (doi: 10.1007/s00180-017-0753-z)

Lazarus, A., Husmeier, D. and Papamarkou, T. (2018) Multiphase MCMC sampling for parameter inference in nonlinear ordinary differential equations. Proceedings of Machine Learning Research, 84, pp. 1252-1260.

Niu, M., Rogers, S. , Filippone, M. and Husmeier, D. (2017) Parameter Inference in Differential Equation Models Using Time Warped Gradient Matching. RSS 2017 Annual Conference, Glasgow, Scotland, 04-07 Sep 2017.

Lazarus, A., Husmeier, D. and Papamarkou, T. (2017) Inference in Complex Systems Using Multi-Phase MCMC Sampling With Gradient Matching Burn-in. In: 32nd International Workshop on Statistical Modelling, Groningen, Netherlands, 03-07 Jul 2017, pp. 52-57.

Aderhold, A., Husmeier, D. and Grzegorczyk, M. (2017) Approximate Bayesian inference in semi-mechanistic models. Statistics and Computing, 27(4), pp. 1003-1040. (doi: 10.1007/s11222-016-9668-8)

Grzegorczyk, M., Aderhold, A. and Husmeier, D. (2017) Targeting Bayes factors with direct-path non-equilibrium thermodynamic integration. Computational Statistics, 32(2), pp. 717-761. (doi: 10.1007/s00180-017-0721-7)

Niu, M., Rogers, S. , Filippone, M. and Husmeier, D. (2017) Parameter Inference in Differential Equation Models of Biopathways using Time Warped Gradient Matching. In: 13th International Conference on Computational Intelligence Methods for Bioinformatics and Biostatistics, Stirling, UK, 01-03 Sep 2016, pp. 145-159. ISBN 9783319678337 (doi: 10.1007/978-3-319-67834-4_12)

Macdonald, B., Niu, M., Rogers, S. , Filippone, M. and Husmeier, D. (2016) Approximate parameter inference in systems biology using gradient matching: a comparative evaluation. BioMedical Engineering OnLine, 15, 80. (doi: 10.1186/s12938-016-0186-x) (PMID:27454253) (PMCID:PMC4959362)

Niu, M., Rogers, S. , Filippone, M. and Husmeier, D. (2016) Fast inference in nonlinear dynamical systems using gradient matching. Proceedings of Machine Learning Research, 48, pp. 1699-1707.

Macdonald, B. and Husmeier, D. (2015) Gradient matching methods for computational inference in mechanistic models for systems biology: a review and comparative analysis. Frontiers in Bioengineering and Biotechnology, 3, 180. (doi: 10.3389/fbioe.2015.00180) (PMID:26636071) (PMCID:PMC4654429)

Macdonald, B., Higham, C. and Husmeier, D. (2015) Controversy in mechanistic modelling with Gaussian processes. Proceedings of Machine Learning Research, 37, pp. 1539-1547.

Noè, U., Filippone, M. and Husmeier, D. (2015) Emulation of ODEs with Gaussian Processes. In: 30th International Workshop on Statistical Modelling, Linz, Austria, 06-10 Jul 2015, pp. 191-194.

Niu, M., Filippone, M., Husmeier, D. and Rogers, S. (2015) Inference in Nonlinear Differential Equations. In: 30th International Workshop on Statistical Modelling, Linz, Austria, 06-10 Jul 2015, pp. 187-190.

Filippone, M. and Engler, R. (2015) Enabling scalable stochastic gradient-based inference for Gaussian processes by employing the Unbiased LInear System SolvEr (ULISSE). Journal of Machine Learning Research: Workshop and Conference Proceedings, 37, pp. 1015-1024.