Gioia, V., Fasiolo, M., Browell, J. and Bellio, R., 2024. Additive Covariance Matrix Models: Modeling Regional Electricity Net-Demand in Great Britain. Journal of the American Statistical Association, pp.1-13.

Amara-Ouali, Yvenn, Matteo Fasiolo, Yannig Goude, and Hui Yan. “Daily peak electrical load forecasting with a multi-resolution approach.” International Journal of Forecasting 39, no. 3 (2023): 1272-1286.

Browell, J. and Fasiolo, M., 2021. Probabilistic Forecasting of Regional Net-load with Conditional Extremes and Gridded NWP. IEEE Transactions on Smart Grid, 12(6), pp.5011-5019.

Capezza, C., Palumbo, B., Goude, Y., Wood, S.N. and Fasiolo, M., 2021. Additive stacking for disaggregate electricity demand forecasting. The Annals of Applied Statistics, 15(2), pp.727-746.

Fasiolo, M., Wood, S.N., Zaffran, M., Nedellec, R. and Goude, Y., 2021. Fast calibrated additive quantile regression. Journal of the American Statistical Association, 116(535), pp.1402-1412.

Fasiolo, M., Wood, S. N., Zaffran, M., Nedellec, R., & Goude, Y. 2021. qgam: Bayesian Nonparametric Quantile Regression Modeling in R. Journal of Statistical Software, 100(9), 1–31. https://doi.org/10.18637/jss.v100.i09

Fasiolo, M., Nedellec, R., Goude, Y. and Wood, S.N., 2019. Scalable visualisation methods for modern Generalized Additive Models. Journal of Computational and Graphical Statistics.

Wood, S.N. and Fasiolo, M., 2017. A generalized Fellner‐Schall method for smoothing parameter optimization with application to Tweedie location, scale and shape models. Biometrics, 73(4), pp.1071-1081.

Ward, D., Beaumont, M. and Fasiolo, M., 2024. SoftCVI: Contrastive variational inference with self-generated soft labels. Accepted as a “spotlight” paper at ICLR 2025, arXiv preprint arXiv:2407.15687.

Ward, D., Cannon, P., Beaumont, M., Fasiolo, M. and Schmon, S., 2022. Robust neural posterior estimation and statistical model criticism. Advances in Neural Information Processing Systems, 35, pp.33845-33859.

Fasiolo, M., de Melo, F.E. and Maskell, S., 2018. Langevin incremental mixture importance sampling. Statistics and Computing, 28(3), pp.549-561.

Fasiolo, M., Wood, S.N., Hartig, F. and Bravington, M.V., 2018. An extended empirical saddlepoint approximation for intractable likelihoods. Electronic Journal of Statistics, 12(1), pp.1544-1578.

Fasiolo, M., Pya, N. and Wood, S.N., 2016. A comparison of inferential methods for highly nonlinear state space models in ecology and epidemiology. Statistical Science, 31(1), pp.96-118.

De Rosa, D., Ballabio, C., Lugato, E., Fasiolo, M., Jones, A. and Panagos, P., 2024. Soil organic carbon stocks in European croplands and grasslands: How much have we lost in the past decade?. Global Change Biology, 30(1), p.e16992.

de Vilmarest, J., Browell, J., Fasiolo, M., Goude, Y. and Wintenberger, O., 2023. Adaptive probabilistic forecasting of electricity (net-) load. IEEE Transactions on Power Systems, 39(2), pp.4154-4163.

Baayen, R.H., Fasiolo, M., Wood, S. and Chuang, Y.Y., 2022. A note on the modeling of the effects of experimental time in psycholinguistic experiments. The Mental Lexicon, 17(2), pp.178-212. arXiv preprint arXiv:2105.13786.

De Rosa, D., Basso, B., Fasiolo, M., Friedl, J., Fulkerson, B., Grace, P.R. and Rowlings, D.W., 2021. Predicting pasture biomass using a statistical model and machine learning algorithm implemented with remotely sensed imagery. Computers and Electronics in Agriculture, 180, p.105880.

Noacco, V., Duffy, C. J., Wagener, T., Worrall, F., Fasiolo, M. and Howden, N.J.K. (2019). Drivers of inter-and intra-annual variability of dissolved organic carbon concentration in the River Thames between 1884 and 2013. Hydrological processes. 33 (6), 994-1012.

Tomaschek, F., Tucker, B.V., Fasiolo, M. and Baayen, R.H., 2018. Practice makes perfect: The consequences of lexical proficiency for articulation. Linguistics Vanguard, 4(s2).