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Items: 1 to 20 of 102

1.
2.

Bayesian analysis for finite mixture in non-recursive non-linear structural equation models.

Li Y, Wang HZ.

Br J Math Stat Psychol. 2010 May;63(Pt 2):361-77. doi: 10.1348/000711009X466367. Epub 2009 Aug 28.

PMID:
19719904
3.

Bayesian inference for finite mixtures of univariate and multivariate skew-normal and skew-t distributions.

Frühwirth-Schnatter S, Pyne S.

Biostatistics. 2010 Apr;11(2):317-36. doi: 10.1093/biostatistics/kxp062. Epub 2010 Jan 27.

PMID:
20110247
4.

Bayesian phylogeny analysis via stochastic approximation Monte Carlo.

Cheon S, Liang F.

Mol Phylogenet Evol. 2009 Nov;53(2):394-403. doi: 10.1016/j.ympev.2009.06.019. Epub 2009 Jul 7.

PMID:
19589389
5.

A general construction for parallelizing Metropolis-Hastings algorithms.

Calderhead B.

Proc Natl Acad Sci U S A. 2014 Dec 9;111(49):17408-13. doi: 10.1073/pnas.1408184111. Epub 2014 Nov 24.

6.

Bayesian adaptive Markov chain Monte Carlo estimation of genetic parameters.

Mathew B, Bauer AM, Koistinen P, Reetz TC, Léon J, Sillanpää MJ.

Heredity (Edinb). 2012 Oct;109(4):235-45. doi: 10.1038/hdy.2012.35. Epub 2012 Jul 18.

7.

Comparison of the performance of particle filter algorithms applied to tracking of a disease epidemic.

Sheinson DM, Niemi J, Meiring W.

Math Biosci. 2014 Sep;255:21-32. doi: 10.1016/j.mbs.2014.06.018. Epub 2014 Jul 9.

PMID:
25016201
8.

Robust Bayesian Analysis of Heavy-tailed Stochastic Volatility Models using Scale Mixtures of Normal Distributions.

Abanto-Valle CA, Bandyopadhyay D, Lachos VH, Enriquez I.

Comput Stat Data Anal. 2010 Dec 1;54(12):2883-2898.

9.

Searching for efficient Markov chain Monte Carlo proposal kernels.

Yang Z, Rodríguez CE.

Proc Natl Acad Sci U S A. 2013 Nov 26;110(48):19307-12. doi: 10.1073/pnas.1311790110. Epub 2013 Nov 11.

11.

Generalized Dynamic Factor Models for Mixed-Measurement Time Series.

Cui K, Dunson DB.

J Comput Graph Stat. 2014 Feb 12;23(1):169-191.

12.

A Comparison of Estimation Methods for a Multi-unidimensional Graded Response IRT Model.

Kuo TC, Sheng Y.

Front Psychol. 2016 Jun 10;7:880. doi: 10.3389/fpsyg.2016.00880. eCollection 2016.

13.

The Polya Tree Sampler: Towards Efficient and Automatic Independent Metropolis-Hastings Proposals.

Hanson TE, Monteiro JV, Jara A.

J Comput Graph Stat. 2011 Mar 1;20(1):41-62.

14.
15.

Parametric and nonparametric population methods: their comparative performance in analysing a clinical dataset and two Monte Carlo simulation studies.

Bustad A, Terziivanov D, Leary R, Port R, Schumitzky A, Jelliffe R.

Clin Pharmacokinet. 2006;45(4):365-83.

PMID:
16584284
16.

Bayesian parameter inference for stochastic biochemical network models using particle Markov chain Monte Carlo.

Golightly A, Wilkinson DJ.

Interface Focus. 2011 Dec 6;1(6):807-20. doi: 10.1098/rsfs.2011.0047. Epub 2011 Sep 29.

17.

Comparing variational Bayes with Markov chain Monte Carlo for Bayesian computation in neuroimaging.

Nathoo FS, Lesperance ML, Lawson AB, Dean CB.

Stat Methods Med Res. 2013 Aug;22(4):398-423. doi: 10.1177/0962280212448973. Epub 2012 May 28.

PMID:
22642986
18.

Bayesian Inference for General Gaussian Graphical Models With Application to Multivariate Lattice Data.

Dobra A, Lenkoski A, Rodriguez A.

J Am Stat Assoc. 2011;106(496):1418-1433. Epub 2012 Dec 24.

19.

Bayesian parameter estimation for the Wnt pathway: an infinite mixture models approach.

Koutroumpas K, Ballarini P, Votsi I, Cournède PH.

Bioinformatics. 2016 Sep 1;32(17):i781-i789. doi: 10.1093/bioinformatics/btw471.

PMID:
27587701
20.

Laplace Variational Approximation for Semiparametric Regression in the Presence of Heteroskedastic Errors.

Bugbee BD, Breidt FJ, van der Woerd MJ.

J Comput Graph Stat. 2016;25(1):225-245. Epub 2016 Mar 9.

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