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 +
 +===== Links =====
 +
 +== Aproximação de Laplace ==
 +  * {{http://​www.stats.ox.ac.uk/​~steffen/​teaching/​bs2HT9/​laplace.pdf|Laplace'​s Method of Integration - Ste en Lauritzen}};​
 +  * {{http://​www.stanford.edu/​~mch/​harding-hausman-laplace.pdf|Using a Laplace Approximation to Estimate the Random Coefficients Logit Model by Non-linear Least Squares}};
 +  * {{http://​www.cemmap.ac.uk/​wps/​cwp0601.pdf|USING A LAPLACE APPROXIMATION TO ESTIMATE THE RANDOM COEFFICIENTS LOGIT MODEL BY NON-LINEAR LEAST SQUARES}};
 +  * {{http://​www.cs.berkeley.edu/​~jordan/​courses/​260-spring10/​lectures/​lecture16.pdf|Laplace approximation review}};
 +  * {{http://​www.cs.toronto.edu/​~mackay/​itprnn/​ps/​343.344.pdf|Laplace'​s Method}};
 +  * {{http://​www.ece.rice.edu/​~vc3/​elec633/​graphical_models_notes_091108.pdf|Laplace Approximation}};​
 +  * {{http://​galton.uchicago.edu/​~pmcc/​pubs/​paper26.pdf|Laplace approximation of high dimensional integrals}};​
 +  * {{http://​support.sas.com/​documentation/​cdl/​en/​statug/​63347/​HTML/​default/​viewer.htm#​statug_glimmix_a0000001432.htm|Maximum Likelihood Estimation Based on Laplace Approximation}};​
 +  * {{http://​statmath.wu.ac.at/​research/​talks/​resources/​MultIRT.pdf|Fitting Multidimensional Latent Variable Models using an Efficient Laplace Approximation}};​
 +  * {{http://​prin08.uniud.it/​tl_files/​prin08/​upload/​papers/​2010_3.pdf|LAPLACE APPROXIMATION IN MEASUREMENT ERROR MODELS}};
 +  * {{http://​www.unc.edu/​~vangelis/​files/​sglmmlapl.pdf|Asymptotic inference for Spatial GLMM using high order Laplace approximation}};​
 +  * {{http://​www.jstor.org/​pss/​1390617}};​
 +  * {{http://​digitalcommons.unl.edu/​cgi/​viewcontent.cgi?​article=1003&​context=statisticsdiss&​sei-redir=1#​search=%22laplace%20approximation%20integral%22|FULLY EXPONENTIAL LAPLACE APPROXIMATION EM ALGORITHM FOR NONLINEAR MIXED EFFECTS MODELS}};
 +  * {{http://​proquest.umi.com/​pqdlink?​Ver=1&​Exp=07-02-2016&​FMT=7&​DID=1188875391&​RQT=309&​attempt=1&​cfc=1|Applications of Laplace approximation for hierarchical generalized linear models in educational research}};
 +  * {{http://​people.math.aau.dk/​~rw/​Undervisning/​Topics/​Handouts/​6.hand.pdf|Computation of the likelihood function for GLMMs}};
 +  * {{http://​www.ansci.wisc.edu/​morota/​beamer/​computing.pdf|Computing:​ Generalized,​ Linear, and Mixed Models}};
 +  * {{http://​jmlr.csail.mit.edu/​papers/​volume12/​cseke11a/​cseke11a.pdf|Approximate Marginals in Latent Gaussian Models}};
 +  * {{http://​biowww.dfci.harvard.edu/​~yili/​spa1.pdf|Modeling Spatial Survival Data Using Semiparametric Frailty Models}};
 +  * {{http://​actuaryzhang.com/​seminar/​topic5_mcmc.pdf|Markov Chain Monte Carlo Methods}};
 +  * 8-O{{http://​dirk.eddelbuettel.com/​blog/​2011/​07/​05/#​rcppeigen_introduction|Even faster linear model fits with R using RcppEigen}};​
 +  * {{http://​dirk.eddelbuettel.com/​blog/​2011/​07/​14/#​rcpp_gibbs_example|MCMC and faster Gibbs Sampling using Rcpp}};
 +  * {{http://​darrenjw.wordpress.com/​2011/​07/​16/​gibbs-sampler-in-various-languages-revisited|Gibbs sampler in various languages (revisited)}};​
 +
 +== Métodos Monte Carlo ==
 +  * {{http://​elsa.berkeley.edu/​reprints/​misc/​understanding.pdf|Understanding the Metropolis-Hastings Algorithm}};​
 +  * {{http://​www.econ.upenn.edu/​~jesusfv/​LectureNotes_7_MH|Metropolis-Hasting Algorithm - Jesús Fernández-Villaverde}};​
 +  * {{http://​www.dme.ufrj.br/​marina/​MCMC.pdf| MCMC - Marina}};
 +  * {{http://​www.maths.bris.ac.uk/​~manpw/​teaching/​folien1.pdf|Monte Carlo Methods: Lecture 1: Introduction - Nick Whiteley}};
 +  * {{http://​www.maths.bris.ac.uk/​~manpw/​teaching/​folien2.pdf|Monte Carlo Methods: Lecture 2: Transformation and Rejection - Nick Whiteley}};
 +  * {{http://​www.maths.bris.ac.uk/​~manpw/​teaching/​folien3.pdf|Monte Carlo Methods: Lecture 3: Importance Sampling - Nick Whiteley}};
 +  * {{http://​www.maths.bris.ac.uk/​~manpw/​teaching/​folien45.pdf|Monte Carlo Methods: Lectures 5 & 6: The Gibbs Sampler - Nick Whiteley}};
 +  * {{http://​www.maths.bris.ac.uk/​~manpw/​teaching/​folien6.pdf|Monte Carlo Methods: Lecture 7: The Metropolis-Hastings Algorithm - Nick Whiteley}};
 +  * {{http://​www.maths.bris.ac.uk/​~manpw/​teaching/​folien78.pdf|Monte Carlo Methods:: Lectures 9 & 10: Combining Kernels, Convergence Diagnostics - Nick Whiteley}};
 +  * {{http://​www.maths.bris.ac.uk/​~manpw/​teaching/​folien9.pdf|Monte Carlo Methods: Reversible Jump MCMC - Nick Whiteley}};
 +  * {{http://​www.maths.bris.ac.uk/​~manpw/​teaching/​notes.pdf|Monte Carlo Methods - Lecture Notes - Edited by Nick Whiteley}};
 +  * {{http://​www.icmc.usp.br/​~ehlers/​SME0809/​praticas/​node18.html|Algoritmo de Metropolis-Hastings}};​
 +  * {{http://​www.people.fas.harvard.edu/​~plam/​teaching/​methods/​mcmc/​mcmc.pdf|MCMC Methods: Gibbs Sampling and the Metropolis-Hastings Algorithm - Patrick Lam}};
 +  * {{http://​www.maths.manchester.ac.uk/​~pneal/​CIS/​CIS2007.html|Computationally Intensive Statistics 2010/​2011}};​
 +  * {{http://​www.maths.manchester.ac.uk/​~pneal/​statscomp.html|Statistical Computing 2010/​2011}};​
 +  * {{http://​www.lisa.stat.vt.edu/?​q=node/​1784|Bayesian Methods for Regression in R - Nels Johnson}};
 +
 +== Algorítmo EM ==
 +  * [[http://​www.leg.ufpr.br/​~paulojus/​EM|Link para diversos artigos e materiais sobre EM]]
 +  * Outros em modelos não lineares:
 +    * [[http://​www.jstor.org/​stable/​2533054|Walker]]:​ An EM Algorithm for Nonlinear Random Effects Models
 +    * [[http://​bmsr.usc.edu/​Core%20Research/​dzd/​6614.pdf|Wang et al.]]: Nonlinear random effects mixture models: Maximum likelihood estimation via the EM algorithm
 +    * [[http://​dl.acm.org/​citation.cfm?​id=1225091|Wang]]:​ EM algorithms for nonlinear mixed effects models
 +    * [[http://​fedc.wiwi.hu-berlin.de/​xplore/​ebooks/​html/​csa/​node45.html|material online]]
  

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