Edinburgh Research Explorer

Institute for Adaptive and Neural Computation

Organisational unit: Research Institute

  1. VIBES: A Variational Inference Engine for Bayesian Networks

    Bishop, C. M., Spiegelhalter, D. J. & Winn, J., 2002, Advances in Neural Information Processing Systems 15 (NIPS 2002). 8 p.

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

  2. Validating a standardised test battery for synesthesia: Does the Synesthesia Battery reliably detect synesthesia?

    Carmichael, D. A., Down, M. P., Shillcock, R. C., Eagleman, D. M. & Simner, J., May 2015, In : Consciousness and Cognition. 33, p. 375-385 11 p.

    Research output: Contribution to journalArticle

  3. Validity conditions for moment closure approximations in stochastic chemical kinetics

    Schnoerr, D., Sanguinetti, G. & Grima, R., 1 Jan 2014, In : Journal of Chemical Physics. 141, 8, 084103.

    Research output: Contribution to journalArticle

  4. Variational Bayesian Model Selection for Mixture Distributions

    Corduneanu, A. & Bishop, C. M., 2001, Proceedings Eighth International Conference on Artificial Intelligence and Statistics. Morgan Kaufmann, p. 27-34 8 p.

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

  5. Variational Estimation in Spatiotemporal Systems From Continuous and Point-Process Observations

    Zammit-Mangion, A., Sanguinetti, G. & Kadirkamanathan, V., 1 Jul 2012, In : IEEE Transactions on Signal Processing. 60, 7, p. 3449-3459 11 p.

    Research output: Contribution to journalArticle

  6. Variational Learning in Graphical Models and Neural Networks

    Bishop, C., 1998, ICANN 98: Proceedings of the 8th International Conference on Artificial Neural Networks, Skövde, Sweden, 2–4 September 1998. Niklasson, L., Boden, M. & Ziemke, T. (eds.). Springer London, p. 13-22 10 p. (Perspectives in Neural Computing).

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

  7. Variational Message Passing

    Winn, J. & Bishop, C., 2005, In : Journal of Machine Learning Research. 6, p. 661-694 34 p.

    Research output: Contribution to journalArticle

  8. Variational Noise-Contrastive Estimation

    Rhodes, B. & Gutmann, M., 25 Apr 2019, Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS 2019). Naha, Okinawa, Japan: PMLR, Vol. 89. p. 2741-2750 14 p.

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

  9. Variational Principal Components

    Bishop, C. M., 1 Jan 1999, Proceedings Ninth International Conference on Artificial Neural Networks, ICANN'99. IEE, p. 509-514 6 p.

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

  10. Variational Relevance Vector Machines

    Bishop, C. M. & Tipping, M. E., 1 Jan 2000, Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence. Morgan Kaufmann, p. 46-53 8 p.

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