Cross-validation based assessment of a new Bayesian palaeoclimate model-Reference-Cited by-同舟云学术

Cross-validation based assessment of a new Bayesian palaeoclimate model

Published:2013-12 Issue:8 Volume:24 Page:550-568
ISSN:1180-4009
Container-title:Environmetrics
language:en
Short-container-title:Environmetrics

Author:

Mukhopadhyay S.¹,Bhattacharya S.²

Affiliation:

1. Southampton Statistical Sciences Research Institute; University of Southampton; Southampton U.K.

2. Bayesian and Interdisciplinary Research Unit; Indian Statistical Institute; 203. B. T. Road Kolkata-700108 India

Publisher

Wiley

Subject

Ecological Modelling,Statistics and Probability

Reference48 articles.

1. Discussion of “Approximate Bayesian Inference for Latent Gaussian Models by Using Integrated Nested Laplace Approximations”;Banerjee;Journal of the Royal Statistical Society: Series B,2008

2. Paleolimnological approaches to climate change with special regard to the biological record;Battarbee;Quaternary Science Reviews,2000

3. P-values for composite null models;Bayarri;Journal of the American Statistical Association,1999

4. Bhattacharya S 2004 Importance resampling MCMC: a methodology for cross-validation in inverse problems and its applications in model assessment Doctoral Thesis

5. A Bayesian semiparametric model for organism based environmental reconstruction;Bhattacharya;Environmetrics,2006

Cited by 6 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Transdimensional transformation based Markov chain Monte Carlo;Brazilian Journal of Probability and Statistics;2019-02-01

2. Disequilibrium multi-dividing ontology learning algorithm;Communications in Statistics - Theory and Methods;2017-05-17

3. Margin based ontology sparse vector learning algorithm and applied in biology science;Saudi Journal of Biological Sciences;2017-01

4. Ontology algorithm using singular value decomposition and applied in multidisciplinary;Cluster Computing;2016-09-21

5. Gradient descent technology for sparse vector learning in ontology algorithms;Journal of Discrete Mathematical Sciences and Cryptography;2016-05-03