Timothy Waite

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Research Beacons, Institutes and Platforms

Digital Futures

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7 Article

Approximate Laplace importance sampling for the estimation of expected Shannon information gain in high-dimensional Bayesian design for nonlinear models
Englezou, Y., Waite, T. & Woods, D. C., 30 Sept 2022, In: Statistics and Computing. 32, 82.
Research output: Contribution to journal › Article › peer-review

Open Access
Efficient forecasting and uncertainty quantification for large scale account level Monte Carlo models of debt recovery
Baynes, S., Cotter, S., Russell, P., Ryan, E. & Waite, T., 23 Oct 2022, (Accepted/In press) In: Journal of the Royal Statistical Society. Series C: Applied Statistics.
Research output: Contribution to journal › Article › peer-review
Minimax efficient random experimental design strategies with application to model-robust design for prediction
Waite, T. & Woods, D., 14 Dec 2020, In: Journal of the American Statistical Association. 0, p. 1-36 36 p.
Research output: Contribution to journal › Article › peer-review
Singular Prior Distributions and Ill-Conditioning in Bayesian D-Optimal Design for Several Nonlinear Models
Waite, T., 1 Jan 2018, In: Statistica Sinica. 28, p. 505-525 21 p.
Research output: Contribution to journal › Article › peer-review

Open Access
Bayesian design of experiments for generalised linear models and dimensional analysis with industrial and scientific application
Woods, D. C., Overstall, A. M., Adamou, M. & Waite, T., 2017, In: Quality Engineering.
Research output: Contribution to journal › Article › peer-review

Open Access

SAU: Statistical Advisory Unit
Boshnakov, G., Charalambous, C., Donev, A., Foster, P., Hall, I., Han, Y., House, T., Johnson, O., Nadarajah, S., Pan, J., Strimmer, K., Waite, T. & Yuan, J.
Project: Research

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Timothy Waite

Personal profile

Research Beacons, Institutes and Platforms

Fingerprint

Research output

Approximate Laplace importance sampling for the estimation of expected Shannon information gain in high-dimensional Bayesian design for nonlinear models

Efficient forecasting and uncertainty quantification for large scale account level Monte Carlo models of debt recovery

Minimax efficient random experimental design strategies with application to model-robust design for prediction

Singular Prior Distributions and Ill-Conditioning in Bayesian D-Optimal Design for Several Nonlinear Models

Bayesian design of experiments for generalised linear models and dimensional analysis with industrial and scientific application

Projects

SAU: Statistical Advisory Unit