Items by Simpson, Daniel

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Number of items: 25.


Rue, H., Riebler, A., Sørbye, S. H., Illian, J. B., Simpson, D. P. and Lindgren, F. K., 2017. Bayesian computing with INLA:a review. Annual Review of Statistics and Its Application, 4, pp. 395-421.

Simpson, D. P., Rue, H., Martins, T. G., Riebler, A. and Sørbye, S. H., 2017. Penalising model component complexity:A principled, practical approach to constructing priors. Statistical Science, 32 (1), pp. 1-28.

Simpson, D., Rue, H., Riebler, A., Martins, T. G. and Sørbye, S. H., 2017. You just keep on pushing my love over the borderline:a rejoinder. Statistical Science, 32 (1), pp. 44-46.

Riebler, A., Sørbye, S. H., Simpson, D. and Rue, H., 2016. An intuitive Bayesian spatial model for disease mapping that accounts for scaling. Statistical Methods in Medical Research, 25 (4), pp. 1145-1165.

Simpson, D., Illian, J. B., Lindgren, F., Sorbye, S. H. and Rue, H., 2016. Going off grid:computationally efficient inference for log-Gaussian Cox processes. Biometrika, 103 (1), pp. 49-70.

Fuglstad, G.-A., Simpson, D., Lindgren, F. and Rue, H., 2015. Does non-stationary spatial data always require non-stationary random fields? Spatial Statistics, 14 (Part C), pp. 505-531.

Simpson, D., Lindgren, F. and Rue, H., 2015. Beyond the valley of the covariance function. Statistical Science, 30 (2), pp. 164-166.

Oates, C. J., Simpson, D. and Girolami, M., 2015. Discussion of "Sequential Quasi-Monte Carlo" by Mathieu Gerber and Nicolas Chopin. Journal of the Royal Statistical Society: Series B - Statistical Methodology

Lyne, A.-M., Girolami, M., Atchadé, Y., Strathmann, H. and Simpson, D., 2015. On Russian Roulette Estimates for Bayesian Inference with Doubly-Intractable Likelihoods. Statistical Science, 30 (4), pp. 443-467.

Yue, Y. R., Simpson, D., Lindgren, F. K. and Rue, H., 2014. Bayesian adaptive smoothing splines using stochastic differential equations. Bayesian Analysis, 9 (2), p. 397.

Aune, E., Simpson, D. P. and Eidsvik, J., 2014. Parameter estimation in high dimensional Gaussian distributions. Statistics and Computing, 24 (2), pp. 247-263.

Nelson, M. R., Sutton, K. J., Brook, B. S., Mallet, D. G., Simpson, D. P. and Rank, R. G., 2014. STI-GMaS:an open-source environment for simulationof sexually-transmitted infections. BMC Systems Biology, 8, p. 66.

Mallet, D. G., Bagher-Oskouei, M., Farr, A. C., Simpson, D. P. and Sutton, K. J., 2013. A mathematical model of chlamydial infection incorporating spatial movement of chlamydial particles. Bulletin of Mathematical Biology, 75 (11), pp. 2257-2270.

Martins, T. G., Simpson, D., Lindgren, F. and Rue, H., 2013. Bayesian computing with INLA:New features. Computational Statistics & Data Analysis, 67, pp. 68-83.

Brännström, Å., Carlsson, L. and Simpson, D., 2013. On the convergence of the Escalator Boxcar Train. SIAM Journal on Numerical Analysis, 51 (6), 3213–3231.

Cameletti, M., Lindgren, F., Simpson, D. and Rue, H., 2012. Spatio-temporal modeling of particulate matter concentration through the SPDE approach. AStA Advances in Statistical Analysis, 97 (2), pp. 109-131.

Lindgren, F., Martins, T., Rue, H. and Simpson, D., 2011. Discussion on "Spatial prediction in the presence of positional error". Environmetrics, 22 (2), p. 127.

Strickland, C. M., Simpson, D. P., Turner, I. W., Denham, R. and Mengersen, K. L., 2011. Fast Bayesian analysis of spatial dynamic factor models for multi-temporal remotely sensed imagery. Journal of the Royal Statistical Society: Series C - Applied Statistics, 60 (1), pp. 109-124.

Ilic, M., Turner, I. W. and Simpson, D. P., 2010. A restarted Lanczos approximation to functions of a symmetric matrix. IMA Journal of Numerical Analysis, 30 (4), pp. 1044-1061.

Simpson, D., Turner, I. W. and Pettitt, A. N., 2008. Sampling from a Gaussian Markov random field conditioned on linear constraints. The ANZIAM Journal, 48 (CTAC 2006), C1041-C1053.

This list was generated on Sun Oct 22 12:16:41 2017 IST.