Edinburgh Research Explorer

Institute for Adaptive and Neural Computation

Organisational unit: Research Institute

  1. Probabilistic inference for solving discrete and continuous state Markov Decision Processes

    Toussaint, M. & Storkey, A., 2006, ICML '06 Proceedings of the 23rd international conference on Machine learning. ACM, p. 945-952 8 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  2. Probabilistic inference for solving (PO) MDPs

    Toussaint, M., Harmeling, S. & Storkey, A., Dec 2006, 22 p.

    Research output: Working paper

  3. Expectation-Maximization Methods for Solving (PO)MDPs and Optimal Control Problems.

    Toussaint, M., Storkey, A. J. & Harmeling, S., 2011, Bayesian Time Series Models. Chiappa, S. & Barber, D. (eds.). Cambridge University Press, p. 388-413 26 p.

    Research output: Chapter in Book/Report/Conference proceedingChapter

  4. Probabilistic Inference over RFID Streams in Mobile Environments

    Tran, T., Sutton, C., Cocci, R., Nie, Y., Diao, Y. & Shenoy, P., 2009, Data Engineering, 2009. ICDE '09. IEEE 25th International Conference on. Institute of Electrical and Electronics Engineers (IEEE), p. 1096-1107 12 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  5. Supporting User-Defined Functions on Uncertain Data

    Tran, T. T. L., Diao, Y., Sutton, C. A. & Liu, A., 2013, In : Proceedings of the VLDB Endowment (PVLDB). 6, 6, p. 469-480 12 p.

    Research output: Contribution to journalArticle

  6. A Bayesian approach for structure learning in oscillating regulatory networks

    Trejo-Banos, D., Millar, A. & Sanguinetti, G., 14 Jul 2015, In : Bioinformatics. 31, 22, p. 3617-3624 8 p.

    Research output: Contribution to journalArticle

  7. Experimental design for inference over the A. thaliana circadian clock network

    Trejo-Banos, D., Millar, A. J. & Sanguinetti, G., 2015, Computational Methods in Systems Biology: 13th International Conference, CMSB 2015, Nantes, France, September 16-18, 2015, Proceedings. Springer International Publishing, p. 28-39 12 p. (Lecture Notes in Computer Science; vol. 9308).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  8. Editorial: Special Issue on Probabilistic Models for Image Understanding

    Triggs, B. & Williams, C. K. I., 10 Jun 2010, In : International Journal of Computer Vision. 88, 2, p. 145-146 2 p.

    Research output: Contribution to journalEditorial

  9. Reprogramming of Escherichia coli K-12 Metabolism during the Initial Phase of Transition from an Anaerobic to a Micro-Aerobic Environment

    Trotter, E. W., Rolfe, M. D., Hounslow, A. M., Craven, C. J., Williamson, M. P., Sanguinetti, G., Poole, R. K. & Green, J., 1 Sep 2011, In : PLoS ONE. 6, 9, p. 1-7 7 p.

    Research output: Contribution to journalArticle

  10. Learning analytics adoption – approaches and maturity

    Tsai, Y-S., Kovanović, V. & Gasevic, D., 3 Jan 2019, (Accepted/In press). 2 p.

    Research output: Contribution to conferencePoster