Zobrazeno 1 - 10
of 305
pro vyhledávání: '"Griffin, Jim"'
Autor:
Bernaciak, Dawid, Griffin, Jim E.
We propose a general-purpose approximation to the Ferguson-Klass algorithm for generating samples from L\'evy processes without Gaussian components. We show that the proposed method is more than 1000 times faster than the standard Ferguson-Klass algo
Externí odkaz:
http://arxiv.org/abs/2407.01483
In this paper, we introduce a novel MCMC sampler, PARNI-DAG, for a fully-Bayesian approach to the problem of structure learning under observational data. Under the assumption of causal sufficiency, the algorithm allows for approximate sampling direct
Externí odkaz:
http://arxiv.org/abs/2311.00599
Developing an efficient computational scheme for high-dimensional Bayesian variable selection in generalised linear models and survival models has always been a challenging problem due to the absence of closed-form solutions for the marginal likeliho
Externí odkaz:
http://arxiv.org/abs/2308.00869
Autor:
Diana, Alex, Matechou, Eleni, Griffin, Jim, Yu, Douglas, Luo, Mingjie, Tosa, Marie, Bush, Alex, Griffiths, Richard
DNA-based biodiversity surveys involve collecting physical samples from survey sites and assaying the contents in the laboratory to detect species via their diagnostic DNA sequences. DNA-based surveys are increasingly being adopted for biodiversity m
Externí odkaz:
http://arxiv.org/abs/2211.12213
Autor:
Luo, Yiyong, Griffin, Jim E.
Vector autoregressions (VARs) are popular model for analyzing multivariate economic time series. However, VARs can be over-parameterized if the numbers of variables and lags are moderately large. Tensor VAR, a recent solution to over-parameterization
Externí odkaz:
http://arxiv.org/abs/2211.01727
Autor:
Beraha, Mario, Griffin, Jim E.
We propose a methodology for modeling and comparing probability distributions within a Bayesian nonparametric framework. Building on dependent normalized random measures, we consider a prior distribution for a collection of discrete random measures w
Externí odkaz:
http://arxiv.org/abs/2205.15654
Autor:
Bernaciak, Dawid, Griffin, Jim E.
Publikováno v:
In International Journal of Forecasting October-December 2024 40(4):1721-1733
Publikováno v:
In Computational Statistics and Data Analysis February 2025 202
Autor:
Bernaciak, Dawid, Griffin, Jim E.
We introduce a Loss Discounting Framework for model and forecast combination which generalises and combines Bayesian model synthesis and generalized Bayes methodologies. We use a loss function to score the performance of different models and introduc
Externí odkaz:
http://arxiv.org/abs/2201.12045