Results for Bayesian networks

Publications & Outputs

1. Analysis of proton bunch parameters in the AWAKE experiment

   AWAKE Collaboration, 24/11/2021, In: Journal of Instrumentation. 16, 11, 23 p., P11031.

   Research output: Contribution to Journal/Magazine › Journal article › peer-review

2. Efficient multiscale imaging of subsurface resistivity with uncertainty quantification using ensemble Kalman inversion

   Tso, C-HM., Iglesias, M., Wilkinson, P., Kuras, O., Chambers, J. & Binley, A., 31/05/2021, In: Geophysical Journal International. 225, 2, p. 887-905 19 p.

   Research output: Contribution to Journal/Magazine › Journal article › peer-review

3. Cardiorespiratory interactions during three different temperatures - a case report

   Lin, A., Zilakos, I., Ugland, N., Andersen, M. B., Bergersen, T. K., Stankovski, T., Stefanovska, A. & Elstad, M., 4/08/2020, 11th Conference of the European Study Group on Cardiovascular Oscillations, ESGCO 2020. IEEE

   Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

4. Uncertainty quantification in classification problems: A Bayesian approach for predicating the effects of further test sampling. 23rd International Congress on Modelling and Simulation - Supporting Evidence-Based Decision Making: The Role of Modelling and Simulation, MODSIM 2019

   Phillipson, J., Blair, G. S., Henrys, P., S., E. (ed.) & Office, CSIRO. CUBIC. EW. NSW. G., 6/12/2019, p. 193-199. 7 p.

   Research output: Contribution to conference - Without ISBN/ISSN › Conference paper › peer-review

5. Hybrid self-organizing feature map (SOM) for anomaly detection in cloud infrastructures using granular clustering based upon value-difference metrics

   Stephanakis, I. M., Chochliouros, I. P., Sfakianakis, E., Shirazi, S. N. & Hutchison, D., 1/08/2019, In: Information Sciences. 494, p. 247-277 31 p.

   Research output: Contribution to Journal/Magazine › Journal article › peer-review

6. Efficient estimation of return value distributions from non-stationary marginal extreme value models using Bayesian inference

   Ross, E., Randell, D., Ewans, K., Feld, G. & Jonathan, P., 15/09/2017, In: Ocean Engineering. 142, p. 315-328 14 p.

   Research output: Contribution to Journal/Magazine › Journal article › peer-review

7. Extreme value estimation using the likelihood-weighted method

   Wada, R., Waseda, T. & Jonathan, P., 15/09/2016, In: Ocean Engineering. 124, p. 241-251 11 p.

   Research output: Contribution to Journal/Magazine › Journal article › peer-review

8. Adaptive testing by Bayesian networks with application to language assessment

   Mangili, F., Bonesana, C., Antonucci, A., Zaffalon, M., Rubegni, E., Addimando, L., J., S. (ed.), A., M. (ed.) & K., P. (ed.), 7/06/2016. 3 p.

   Research output: Contribution to conference - Without ISBN/ISSN › Conference paper › peer-review

9. Statistics of extreme ocean environments: Non-stationary inference for directionality and other covariate effects

   Jones, M., Randell, D., Ewans, K. & Jonathan, P., 1/06/2016, In: Ocean Engineering. 119, p. 30-46 17 p.

   Research output: Contribution to Journal/Magazine › Journal article › peer-review

10. Statistical modelling of extreme ocean environments for marine design: A review

    Jonathan, P. & Ewans, K., 2013, In: Ocean Engineering. 62, p. 91-109 19 p.

    Research output: Contribution to Journal/Magazine › Journal article › peer-review

11. Design of an optimal Bayesian incentive compatible broadcast protocol for ad hoc networks with rational nodes

    Suri, N. & Narahari, Y., 2008, In: IEEE Journal on Selected Areas in Communications. 26, 7, p. 1138-1148 11 p.

    Research output: Contribution to Journal/Magazine › Journal article › peer-review

More results »