Zobrazeno 1 - 10
of 4 394
pro vyhledávání: '"Sisson, S. A."'
Artificial neural networks (ANNs) are highly flexible predictive models. However, reliably quantifying uncertainty for their predictions is a continuing challenge. There has been much recent work on "recalibration" of predictive distributions for ANN
Externí odkaz:
http://arxiv.org/abs/2403.05756
This paper presents asymptotic results for the maximum likelihood and restricted maximum likelihood (REML) estimators within a two-way crossed mixed effect model as the sizes of the rows, columns, and cells tend to infinity. Under very mild condition
Externí odkaz:
http://arxiv.org/abs/2401.06446
Max-stable processes are a popular tool for the study of environmental extremes, and the extremal skew-$t$ process is a general model that allows for a flexible extremal dependence structure. For inference on max-stable processes with high-dimensiona
Externí odkaz:
http://arxiv.org/abs/1907.10187
Likelihood-free methods such as approximate Bayesian computation (ABC) have extended the reach of statistical inference to problems with computationally intractable likelihoods. Such approaches perform well for small-to-moderate dimensional problems,
Externí odkaz:
http://arxiv.org/abs/1906.04347
This Chapter, "High-dimensional ABC", is to appear in the forthcoming Handbook of Approximate Bayesian Computation (2018). It details the main ideas and concepts behind extending ABC methods to higher dimensions, with supporting examples and illustra
Externí odkaz:
http://arxiv.org/abs/1802.09725
This Chapter, "Overview of Approximate Bayesian Computation", is to appear as the first chapter in the forthcoming Handbook of Approximate Bayesian Computation (2018). It details the main ideas and concepts behind ABC methods with many examples and i
Externí odkaz:
http://arxiv.org/abs/1802.09720
Autor:
Fan, Y., Sisson, S. A.
This Chapter, "ABC Samplers", is to appear in the forthcoming Handbook of Approximate Bayesian Computation (2018). It details the main ideas and algorithms used to sample from the ABC approximation to the posterior distribution, including methods bas
Externí odkaz:
http://arxiv.org/abs/1802.09650
A new recalibration post-processing method is presented to improve the quality of the posterior approximation when using Approximate Bayesian Computation (ABC) algorithms. Recalibration may be used in conjunction with existing post-processing methods
Externí odkaz:
http://arxiv.org/abs/1704.06374
We propose a novel Bayesian nonparametric method for hierarchical modelling on a set of related density functions, where grouped data in the form of samples from each density function are available. Borrowing strength across the groups is a major cha
Externí odkaz:
http://arxiv.org/abs/1410.8276
Statistical extreme value theory is concerned with the use of asymptotically motivated models to describe the extreme values of a process. A number of commonly used models are valid for observed data that exceed some high threshold. However, in pract
Externí odkaz:
http://arxiv.org/abs/1311.2994