155 citations · 360 across the 13 of their papers we have counts for
16 papers
Adaptive Probabilistic Forecasting of Electricity (Net-)Load
Joseph de Vilmarest, Jethro Browell, Matteo Fasiolo +2
Electricity load forecasting is a necessary capability for power system operators and electricity market participants. The proliferation of local generation, demand response, and e…
Robust Neural Posterior Estimation and Statistical Model Criticism
Daniel Ward, Patrick Cannon, Mark Beaumont +2
Computer simulations have proven a valuable tool for understanding complex phenomena across the sciences. However, the utility of simulators for modelling and forecasting purposes…
Daily peak electrical load forecasting with a multi-resolution approach
Yvenn Amara-Ouali, Matteo Fasiolo, Yannig Goude +1
In the context of smart grids and load balancing, daily peak load forecasting has become a critical activity for stakeholders of the energy industry. An understanding of peak magni…
A note on the modeling of the effects of experimental time in psycholinguistic experiments
R. Harald Baayen, Matteo Fasiolo, Simon Wood +1
Thul et al. (2020) called attention to problems that arise when chronometric experiments implementing specific factorial designs are analysed with the generalized additive mixed mo…
Probabilistic Forecasting of Regional Net-load with Conditional Extremes and Gridded NWP
Jethro Browell, Matteo Fasiolo
The increasing penetration of embedded renewables makes forecasting net-load, consumption less embedded generation, a significant and growing challenge. Here a framework for produc…
qgam: Bayesian non-parametric quantile regression modelling in R
Matteo Fasiolo, Simon N. Wood, Margaux Zaffran +2
Generalized additive models (GAMs) are flexible non-linear regression models, which can be fitted efficiently using the approximate Bayesian methods provided by the mgcv R package.…
Additive stacking for disaggregate electricity demand forecasting
Christian Capezza, Biagio Palumbo, Yannig Goude +2
Future grid management systems will coordinate distributed production and storage resources to manage, in a cost effective fashion, the increased load and variability brought by th…
COVID-19 and the difficulty of inferring epidemiological parameters from clinical data
Simon N. Wood, Ernst C. Wit, Matteo Fasiolo +1
Knowing the infection fatality ratio (IFR) is of crucial importance for evidence-based epidemic management: for immediate planning; for balancing the life years saved against the l…
Scalable visualisation methods for modern Generalized Additive Models
Matteo Fasiolo, Raphaël Nedellec, Yannig Goude +1
In the last two decades the growth of computational resources has made it possible to handle Generalized Additive Models (GAMs) that formerly were too costly for serious applicatio…
Fast calibrated additive quantile regression
M. Fasiolo, S. N. Wood, M. Zaffran +2
We propose a novel framework for fitting additive quantile regression models, which provides well calibrated inference about the conditional quantiles and fast automatic estimation…
Langevin Incremental Mixture Importance Sampling
Matteo Fasiolo, Flávio Eler de Melo, Simon Maskell
This work proposes a novel method through which local information about the target density can be used to construct an efficient importance sampler. The backbone of the proposed me…
A generalized Fellner-Schall method for smoothing parameter estimation with application to Tweedie location, scale and shape models
Simon N. Wood, Matteo Fasiolo
We consider the estimation of smoothing parameters and variance components in models with a regular log likelihood subject to quadratic penalization of the model coefficients, via…
An Extended Empirical Saddlepoint Approximation for Intractable Likelihoods
Matteo Fasiolo, Simon N. Wood, Florian Hartig +1
The challenges posed by complex stochastic models used in computational ecology, biology and genetics have stimulated the development of approximate approaches to statistical infer…
Approximate methods for dynamic ecological models
Matteo Fasiolo, Simon N. Wood
This document is due to appear as a chapter of the forthcoming Handbook of Approximate Bayesian Computation (ABC) by S. Sisson, L. Fan, and M. Beaumont. Here we describe some of th…
Stochastic Particle Flow for Nonlinear High-Dimensional Filtering Problems
Flávio Eler De Melo, Simon Maskell, Matteo Fasiolo +1
A series of novel filters for probabilistic inference that propose an alternative way of performing Bayesian updates, called particle flow filters, have been attracting recent inte…
A comparison of inferential methods for highly non-linear state space models in ecology and epidemiology
Matteo Fasiolo, Natalya Pya, Simon N. Wood
Highly non-linear, chaotic or near chaotic, dynamic models are important in fields such as ecology and epidemiology: for example, pest species and diseases often display highly non…