Cristin

2017
1.	Cortes, Renan X.; Martins, Thiago Guerrera; Prates, Marcos Oliviera; Silva, Bráulio F.A.O.. Inference on dynamic models for non-gaussian random fields using INLA. Brazilian Journal of Probability and Statistics 2017 ;Volum 31.(1) s. 1-23 NTNU Untitled

2.	Simpson, Daniel; Rue, Håvard; Riebler, Andrea Ingeborg; Martins, Thiago Guerrera; Sørbye, Sigrunn Holbek. Penalising Model Component Complexity: A Principled, Practical Approach to Constructing Priors. Statistical Science 2017 ;Volum 32.(1) s. 1-28 UiT NTNU Untitled

3.	Simpson, Daniel; Rue, Håvard; Riebler, Andrea Ingeborg; Martins, Thiago Guerrera; Sørbye, Sigrunn Holbek. You Just Keep on Pushing My Love over the Borderline: A Rejoinder. Statistical Science 2017 ;Volum 32.(1) s. 44-46 NTNU UiT Untitled
2015
4.	Roos, M; Martins, Thiago Guerrera; Held, Leonhard; Rue, Håvard. Sensitivity analysis for Bayesian hierarchical models. Bayesian Analysis 2015 ;Volum 10.(2) s. 321-349 NTNU Untitled
2014
5.	Martins, Thiago Guerrera; Rue, Håvard. Extending Integrated Nested Laplace Approximation to a class of near-Gaussian latent models. Scandinavian Journal of Statistics 2014 ;Volum 41.(4) s. 893-912 NTNU Untitled

6.	Martins, Thiago Guerrera; Simpson, Daniel Peter; Riebler, Andrea; Rue, Håvard; Sørbye, Sigrunn Holbek. Penalising model component complexity: A principled, practical approach to constructing priors. arXiv:1403.4630: ArXiv 2014 34 s. NTNU UiT Untitled

7.	Riebler, Andrea; Martins, Thiago Guerrera; Simpson, Daniel Peter; Guo, Jingyi; Rue, Håvard; Sørbye, Sigrunn Holbek. Penalising model component complexity:A principled, practical approach for constructing priors -- An application to bivariate meta-analysis. Bayesian Biostatistics 2014; 2014-07-02 - 2014-07-05 NTNU UiT Untitled

8.	; Martins, Thiago Guerrera. Bayesian Analysis of latent Gaussian models. : NTNU 2014 239 s. NTNU Untitled
2013
9.	Martins, Thiago Guerrera. Bayesian computing with INLA. 10th Applied Statistics Conference; 2013-09-22 - 2013-09-26 NTNU Untitled

10.	Martins, Thiago Guerrera. Prior for flexibility parameters. 3rd Workshop on Bayesian Inference for Latent Gaussian Models with Applications; 2013-09-12 - 2013-09-14 NTNU Untitled

11.	Martins, Thiago Guerrera. Towards more flexible models in R-INLA. NTNU workshop; 2013-03-10 - 2013-03-11 NTNU Untitled

12.	Martins, Thiago Guerrera; Rue, Håvard. Extending INLA to a class of near-Gaussian latent models. : arXiv 2013 28 s. NTNU UiT Untitled

13.	Martins, Thiago Guerrera; Simpson, Daniel Peter; Illian, Janina; Rue, Håvard; Geirsson, Oli. Discussion of "Beyond mean regression". Statistical Modelling 2013 ;Volum 13.(4) s. 355-361 UiT NTNU Untitled

14.	Martins, Thiago Guerrera; Simpson, Daniel Peter; Lindgren, Finn Kristian; Rue, Håvard. Bayesian computing with INLA: New features. Computational Statistics & Data Analysis 2013 ;Volum 67. s. 68-83 NTNU Untitled
2012
15.	Martins, Thiago Guerrera. Bayesian computing with INLA. 2nd Workshop on Bayesian Inference for Latent Gaussian Models with Applications; 2012-05-30 - 2012-06-01 NTNU Untitled

16.	Martins, Thiago Guerrera. Bayesian computing with INLA. 33rd Annual Conference of the International Society for Clinical Biostatistics; 2012-08-19 - 2012-08-23 NTNU Untitled

17.	Martins, Thiago Guerrera. Extending INLA to a class of near-Gaussian latent models. 2nd Workshop on Bayesian Inference for Latent Gaussian Models with Applications; 2012-05-30 - 2012-06-01 NTNU Untitled

18.	Martins, Thiago Guerrera. Extending INLA to a class of near-Gaussian latent models. Bayesian Inference and Stochastic Computation 2012; 2012-06-22 - 2012-06-23 NTNU Untitled

19.	Martins, Thiago Guerrera. Extending INLA to a class of near-Gaussian latent models. ISBA 2012 World Meeting; 2012-06-25 - 2012-06-29 NTNU Untitled
2011
20.	Lindgren, Finn Kristian; Martins, Thiago Guerrera; Rue, Håvard; Simpson, Daniel Peter. Spatial prediction in the presence of positional error Disscusion. Environmetrics 2011 ;Volum 22.(2) s. 127-127 NTNU Untitled

21.	Martins, Thiago Guerrera; Rue, Håvard; Simpson, Daniel Peter. Riemann manifold langevin and hamiltonian monte carlo methods discussion. Journal of The Royal Statistical Society Series B-statistical Methodology 2011 UiT NTNU Untitled