Cargando…

Identifying influential observations in Bayesian models by using Markov chain Monte Carlo

In statistical modelling, it is often important to know how much parameter estimates are influenced by particular observations. An attractive approach is to re-estimate the parameters with each observation deleted in turn, but this is computationally demanding when fitting models by using Markov cha...

Descripción completa

Detalles Bibliográficos
Autores principales:	Jackson, Dan, White, Ian R, Carpenter, James
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	John Wiley & Sons, Ltd 2012
Materias:	Special Issue Papers
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3500673/ https://www.ncbi.nlm.nih.gov/pubmed/21905065 http://dx.doi.org/10.1002/sim.4356

Ejemplares similares

Fitting mechanistic epidemic models to data: A comparison of simple Markov chain Monte Carlo approaches
por: Li, Michael, et al.
Publicado: (2018)

Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference
por: Gamerman, Dani, et al.
Publicado: (2006)

Bayesian and Markov chain Monte Carlo methods for identifying nonlinear systems in the presence of uncertainty
por: Green, P. L., et al.
Publicado: (2015)

Bayesian Genome Assembly and Assessment by Markov Chain Monte Carlo Sampling
por: Howison, Mark, et al.
Publicado: (2014)

Markov chain Monte Carlo in practice
por: Gilks, W R, et al.
Publicado: (1995)

Markov chain Monte Carlo in practice /
Publicado: (1996)

Handbook of Markov chain Monte Carlo
por: Brooks, Steve
Publicado: (2011)

Etymologia: Markov Chain Monte Carlo
por: Henry, Ronnie
Publicado: (2019)

PHYSTAT Seminar: Accelerating Bayesian Computation: Parallelizing Markov Chain Monte Carlo
por: Caldwell, Allen
Publicado: (2020)

Correction: Bayesian Genome Assembly and Assessment by Markov Chain Monte Carlo Sampling
Publicado: (2014)

Markov chains: analytic and Monte Carlo computations
por: Graham, Carl
Publicado: (2014)

Bayesian Multiple Emitter Fitting using Reversible Jump Markov Chain Monte Carlo
por: Fazel, Mohamadreza, et al.
Publicado: (2019)

Massively parallel Markov chain Monte Carlo with BAT
por: Beaujean, Frederik
Publicado: (2012)

Applying diffusion-based Markov chain Monte Carlo
por: Herbei, Radu, et al.
Publicado: (2017)

Adaptive Incremental Mixture Markov Chain Monte Carlo
por: Maire, Florian, et al.
Publicado: (2019)

An Efficient Independence Sampler for Updating Branches in Bayesian Markov chain Monte Carlo Sampling of Phylogenetic Trees
por: Aberer, Andre J., et al.
Publicado: (2016)

Markov chains: Gibbs fields, Monte Carlo simulation, and queues
por: Brémaud, P
Publicado: (2001)

Markov chains: Gibbs fields, Monte Carlo simulation and queues
por: Brémaud, Pierre
Publicado: (2020)

A simple introduction to Markov Chain Monte–Carlo sampling
por: van Ravenzwaaij, Don, et al.
Publicado: (2016)

Markov chain Monte Carlo for active module identification problem
por: Alexeev, Nikita, et al.
Publicado: (2020)

On the inference of complex phylogenetic networks by Markov Chain Monte-Carlo
por: Rabier, Charles-Elie, et al.
Publicado: (2021)

Parallel Markov chain Monte Carlo - bridging the gap to high-performance Bayesian computation in animal breeding and genetics
por: Wu, Xiao-Lin, et al.
Publicado: (2012)

Use of the reversible jump Markov chain Monte Carlo algorithm to select multiplicative terms in the AMMI-Bayesian model
por: da Silva, Carlos Pereira, et al.
Publicado: (2023)

Identifying category representations for complex stimuli using discrete Markov chain Monte Carlo with people
por: Hsu, Anne S., et al.
Publicado: (2019)

Seriation in Paleontological Data Using Markov Chain Monte Carlo Methods
por: Puolamäki, Kai, et al.
Publicado: (2006)

Comprehensive benchmarking of Markov chain Monte Carlo methods for dynamical systems
por: Ballnus, Benjamin, et al.
Publicado: (2017)

Markov Chain Monte Carlo Methods for Fluid Flow Forecasting in the Subsurface
por: Ali, Alsadig, et al.
Publicado: (2020)

Posterior-based proposals for speeding up Markov chain Monte Carlo
por: Pooley, C. M., et al.
Publicado: (2019)

Model Order Identification for Cable Force Estimation Using a Markov Chain Monte Carlo-Based Bayesian Approach
por: Zhan, Shaodong, et al.
Publicado: (2018)

Bayesian Classification of Proteomics Biomarkers from Selected Reaction Monitoring Data using an Approximate Bayesian Computation-Markov Chain Monte Carlo Approach
por: Nagaraja, Kashyap, et al.
Publicado: (2018)

MCMC-ODPR: Primer design optimization using Markov Chain Monte Carlo sampling
por: Kitchen, James L, et al.
Publicado: (2012)

A comparison of strategies for Markov chain Monte Carlo computation in quantitative genetics
por: Waagepetersen, Rasmus, et al.
Publicado: (2008)

Recombination operators and selection strategies for evolutionary Markov Chain Monte Carlo algorithms
por: Drugan, Madalina M., et al.
Publicado: (2010)

Advanced Markov chain Monte Carlo methods: learning from past samples
por: Liang, Faming, et al.
Publicado: (2010)

Cascade source inference in networks: a Markov chain Monte Carlo approach
por: Zhai, Xuming, et al.
Publicado: (2015)

Synchrotron imaging and Markov Chain Monte Carlo reveal tooth mineralization patterns
por: Green, Daniel R., et al.
Publicado: (2017)

An Auxiliary Variable Method for Markov Chain Monte Carlo Algorithms in High Dimension
por: Marnissi, Yosra, et al.
Publicado: (2018)

Properties of Markov Chain Monte Carlo Performance across Many Empirical Alignments
por: Harrington, Sean M, et al.
Publicado: (2020)

Markov chain Monte Carlo methods for hierarchical clustering of dynamic causal models
por: Yao, Yu, et al.
Publicado: (2021)

Image analysis, random fields and Markov chain Monte Carlo methods : a mathematical introduction
por: Winkler, Gerhard 1946-
Publicado: (2003)

Cannot write session to /tmp/vufind_sessions/sess_qp7ehse0tc66tb8ib48n7lt3ve