Zobrazeno 1 - 10
of 250
pro vyhledávání: '"Zammit Mangion, Andrew"'
Parameter inference for linear and non-Gaussian state space models is challenging because the likelihood function contains an intractable integral over the latent state variables. While Markov chain Monte Carlo (MCMC) methods provide exact samples fr
Externí odkaz:
http://arxiv.org/abs/2406.15998
Simulation-based methods for statistical inference have evolved dramatically over the past 50 years, keeping pace with technological advancements. The field is undergoing a new revolution as it embraces the representational capacity of neural network
Externí odkaz:
http://arxiv.org/abs/2404.12484
Autor:
Zammit-Mangion, Andrew, Kaminski, Michael D., Tran, Ba-Hien, Filippone, Maurizio, Cressie, Noel
interpretable, and well understood models that are routinely employed even though, as is revealed through prior and posterior predictive checks, these can poorly characterise the spatial heterogeneity in the underlying process of interest. Here, we p
Externí odkaz:
http://arxiv.org/abs/2311.09491
Neural Bayes estimators are neural networks that approximate Bayes estimators in a fast and likelihood-free manner. Although they are appealing to use with spatial models, where estimation is often a computational bottleneck, neural Bayes estimators
Externí odkaz:
http://arxiv.org/abs/2310.02600
Making inference with spatial extremal dependence models can be computationally burdensome since they involve intractable and/or censored likelihoods. Building on recent advances in likelihood-free inference with neural Bayes estimators, that is, neu
Externí odkaz:
http://arxiv.org/abs/2306.15642
Models with random effects, such as generalised linear mixed models (GLMMs), are often used for analysing clustered data. Parameter inference with these models is difficult because of the presence of cluster-specific random effects, which must be int
Externí odkaz:
http://arxiv.org/abs/2306.00389
Publikováno v:
Atmospheric Environment, 221, 117091 (2020)
The last decade has seen an explosion in data sources available for the monitoring and prediction of environmental phenomena. While several inferential methods have been developed that make predictions on the underlying process by combining these dat
Externí odkaz:
http://arxiv.org/abs/2303.02050
Normalizing flows are objects used for modeling complicated probability density functions, and have attracted considerable interest in recent years. Many flexible families of normalizing flows have been developed. However, the focus to date has large
Externí odkaz:
http://arxiv.org/abs/2301.06404
Autor:
Bertolacci, Michael, Zammit-Mangion, Andrew, Schuh, Andrew, Bukosa, Beata, Fisher, Jenny, Cao, Yi, Kaushik, Aleya, Cressie, Noel
The natural cycles of the surface-to-atmosphere fluxes of carbon dioxide (CO$_2$) and other important greenhouse gases are changing in response to human influences. These changes need to be quantified to understand climate change and its impacts, but
Externí odkaz:
http://arxiv.org/abs/2210.10479