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Research

Results for Uncertainty quantification

Publications & Outputs

  1. Bayes linear analysis for ordinary differential equations

    Jones, M., Goldstein, M., Randell, D. & Jonathan, P., 30/09/2021, In: Computational Statistics and Data Analysis. 161, 27 p., 107228.

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

  2. Quantifying Uncertainty for Estimates Derived from Error Matrices in Land Cover Mapping Applications: The Case for a Bayesian Approach

    Phillipson, J., Blair, G. & Henrys, P., 5/02/2020, Environmental Software Systems. Data Science in Action: 13th IFIP WG 5.11 International Symposium, ISESS 2020, Wageningen, The Netherlands, February 5–7, 2020, Proceedings. Athanasiadis, I. N., Frysinger, S. P., Schimak, G. & Knibbe, W. J. (eds.). Cham: Springer, p. 151-164 14 p. (IFIP Advances in Information and Communication Technology; vol. 554).

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

  3. Uncertainty quantification in classification problems: A Bayesian approach for predicting the effects of further test sampling

    Phillipson, J., Blair, G. & Henrys, P., 6/12/2019, Proceedings of MODSIM2019, 23rd International Congress on Modelling and Simulation. Elsawah, S. (ed.). Canberra: Modelling and Simulation Society of Australia and New Zealand (MSSANZ), p. 193-199 7 p.

    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. Bayesian Dynamic Linear Models for Estimation of Phenological Events from Remote Sensing Data

    Johnson, M., Caragea, P. C., Meiring, W., Jeganathan, C. & Atkinson, P. M., 1/03/2019, In: Journal of Agricultural, Biological, and Environmental Statistics. 24, 1, p. 1-25 25 p.

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

  6. Bayes linear analysis of risks in sequential optimal design problems

    Jones, M., Goldstein, M., Jonathan, P. & Randell, D., 31/12/2018, In: Electronic Journal of Statistics. 12, 2, p. 4002-4031 30 p.

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

  7. Multi-dimensional predictive analytics for risk estimation of extreme events

    Raghupathi, L., Randell, D., Ross, E., Ewans, K. C. & Jonathan, P., 19/12/2016, 2016 IEEE 23rd International Conference on High Performance Computing Workshops (HiPCW). IEEE, p. 60-69 10 p.

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