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ORCIDJim E. Griffin
Person information
- affiliation: University of Kent, Canterbury, UK
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2020 – today
- 2025
[j17]José A. Perusquía
, Jim E. Griffin, Cristiano Villa:
Beta-CoRM: A Bayesian approach for n-gram profiles analysis. Comput. Stat. Data Anal. 202: 108056 (2025)- 2023
- [j16]Xitong Liang, Samuel Livingstone, Jim E. Griffin:
Adaptive MCMC for Bayesian Variable Selection in Generalised Linear Models and Survival Models. Entropy 25(9): 1310 (2023) - [c1]Xitong Liang, Alberto Caron, Samuel Livingstone, Jim E. Griffin:
Structure Learning with Adaptive Random Neighborhood Informed MCMC. NeurIPS 2023 - [i3]Alberto Caron, Xitong Liang, Samuel Livingstone, Jim E. Griffin:
Structure Learning with Adaptive Random Neighborhood Informed MCMC. CoRR abs/2311.00599 (2023) - 2022
- [j15]Xitong Liang, Samuel Livingstone, Jim E. Griffin:
Adaptive random neighbourhood informed Markov chain Monte Carlo for high-dimensional Bayesian variable selection. Stat. Comput. 32(5): 84 (2022) - 2021
- [j14]Kitty Yuen Yi Wan, Jim E. Griffin:
An adaptive MCMC method for Bayesian variable selection in logistic and accelerated failure time regression models. Stat. Comput. 31(1): 6 (2021) - 2020
- [j13]Nathan Cunningham, Jim E. Griffin, David L. Wild:
ParticleMDI: particle Monte Carlo methods for the cluster analysis of multiple datasets with applications to cancer subtype identification. Adv. Data Anal. Classif. 14(2): 463-484 (2020) - [i2]José A. Perusquía, Jim E. Griffin, Cristiano Villa:
Bayesian Models Applied to Cyber Security Anomaly Detection Problems. CoRR abs/2003.10360 (2020) - [i1]José A. Perusquía, Jim E. Griffin, Cristiano Villa:
On a Bayesian Approach to Malware Detection and Classification through n-gram Profiles. CoRR abs/2011.11558 (2020)
2010 – 2019
- 2018
- [j12]Jim E. Griffin, Maria Kalli, Mark F. J. Steel:
Discussion of "Nonparametric Bayesian Inference in Applications": Bayesian nonparametric methods in econometrics. Stat. Methods Appl. 27(2): 207-218 (2018) - 2017
- [j11]Jim E. Griffin:
Sequential Monte Carlo methods for mixtures with normalized random measures with independent increments priors. Stat. Comput. 27(1): 131-145 (2017) - 2016
- [j10]Jim E. Griffin:
An adaptive truncation method for inference in Bayesian nonparametric models. Stat. Comput. 26(1-2): 423-441 (2016) - 2013
- [j9]E.-I. Delatola, Jim E. Griffin:
A Bayesian semiparametric model for volatility with a leverage effect. Comput. Stat. Data Anal. 60: 97-110 (2013) - [j8]M. Kolossiatis, Jim E. Griffin, M. F. J. Steel:
On Bayesian nonparametric modelling of two correlated distributions. Stat. Comput. 23(1): 1-15 (2013) - [j7]Jim E. Griffin, Stephen G. Walker:
On adaptive Metropolis-Hastings methods. Stat. Comput. 23(1): 123-134 (2013) - 2012
- [j6]Paul D. W. Kirk, Jim E. Griffin, Richard S. Savage, Zoubin Ghahramani, David L. Wild:
Bayesian correlated clustering to integrate multiple datasets. Bioinform. 28(24): 3290-3297 (2012) - [j5]Demetris Lamnisos, Jim E. Griffin, M. F. J. Steel:
Cross-validation prior choice in Bayesian probit regression with many covariates. Stat. Comput. 22(2): 359-373 (2012) - 2011
- [j4]M. Kolossiatis, Jim E. Griffin, M. F. J. Steel:
Modeling overdispersion with the normalized tempered stable distribution. Comput. Stat. Data Anal. 55(7): 2288-2301 (2011) - [j3]Maria Kalli, Jim E. Griffin, Stephen G. Walker:
Slice sampling mixture models. Stat. Comput. 21(1): 93-105 (2011) - 2010
- [j2]Richard S. Savage, Zoubin Ghahramani, Jim E. Griffin, Bernard J. de la Cruz, David L. Wild:
Discovering transcriptional modules by Bayesian data integration. Bioinform. 26(12): 158-167 (2010) - [j1]Jim E. Griffin, M. F. J. Steel:
Bayesian inference with stochastic volatility models using continuous superpositions of non-Gaussian Ornstein-Uhlenbeck processes. Comput. Stat. Data Anal. 54(11): 2594-2608 (2010)
Coauthor Index
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