About Elias Teixeira Krainski Elias Teixeira Krainski Research Scientist, Statistics Bayesian computational statistics geospatial statistics spatio-temporal statistics Elias' research focuses on efficient Bayesian methods for practical implementation, spatio and spatio-temporal statistics, INLA. Articles Related News May 2026 Pioneering Public Health Frontiers 1 min read · Tue, May 19 2026 News INLA Public Health Spatial and spatio-temporal statistics Latent variable models Structural equation models It has been an exhilarating week of international scientific exchange for our research group. From May 11th to 15th, 2026, members of our team had the distinct honour of travelling to Fudan University in Shanghai, on the invitation of Professor Dr. Zhijie Zhang. The trip aims to lay the groundwork for a highly impactful, cross-border partnership in public health analytics. INLA Team Publishes New Book on Joint Survival and Longitudinal Modelling 1 min read · Tue, May 5 2026 News INLA Longitudinal Models Survival analysis We are thrilled to announce the publication of our latest book, a comprehensive guide to fitting complex Bayesian survival, longitudinal, and joint models using the Integrated Nested Laplace Approximations (INLA) methodology. This highly anticipated release represents a major milestone for our team, offering a powerful, computationally efficient alternative to traditional MCMC methods for researchers around the globe. April 2026 Dr. Krainski and Dr. Esmail Lead INLA Training and Showcase Novel Graph-Based Correlation Models in Montpellier 1 min read · Sun, Apr 19 2026 News INLA The BAYESCOMP research group continues to amplify its global scientific footprint, demonstrating leadership in advanced statistical computing and Bayesian inference through a highly successful recent visit to Montpellier. March 2026 Dr. Elias Krainski Leads INLA Course in Brazil 1 min read · Mon, Mar 30 2026 Spotlight R-INLA INLA Bayesian and computational Statistics Demonstrating our research group’s ongoing commitment to international collaboration and knowledge sharing, Dr. Elias Krainski recently concluded a successful five-day online short course on advanced statistical modelling for two premier Brazilian institutions: Universidade Federal de Minas Gerais (UFMG) and Universidade Federal do Paraná (UFPR). January 2023 INLA course at UPNA 1 min read · Tue, Jan 31 2023 News Prof. Haavard Rue and Dr. Elias Krainski presented a course on Bayesian spatial and spatio-temporal modeling with R-INLA at the Public University of Navarre (UPNA) in Pamplona, Spain during 25-27 January 2023. INLA workshop at University of Glasgow 1 min read · Tue, Jan 31 2023 News Prof. Haavard Rue and Dr. Elias Krainski presented a workshop for INLA users and developers titled "INLA: past, present and future" at the University of Glasgow during 18-19 January. June 2022 INLA course at Bordeaux population health center 1 min read · Wed, Jun 15 2022 News Some members of the INLA team presented an INLA short course to the Biostatistics group at the Bordeaux population health center of INSERM and the University of Bordeaux. The content was tailored for biostatistics and public health applications, to avail INLA as a tool for fast Bayesian inference of applicable statistical models.
Pioneering Public Health Frontiers 1 min read · Tue, May 19 2026 News INLA Public Health Spatial and spatio-temporal statistics Latent variable models Structural equation models It has been an exhilarating week of international scientific exchange for our research group. From May 11th to 15th, 2026, members of our team had the distinct honour of travelling to Fudan University in Shanghai, on the invitation of Professor Dr. Zhijie Zhang. The trip aims to lay the groundwork for a highly impactful, cross-border partnership in public health analytics.
INLA Team Publishes New Book on Joint Survival and Longitudinal Modelling 1 min read · Tue, May 5 2026 News INLA Longitudinal Models Survival analysis We are thrilled to announce the publication of our latest book, a comprehensive guide to fitting complex Bayesian survival, longitudinal, and joint models using the Integrated Nested Laplace Approximations (INLA) methodology. This highly anticipated release represents a major milestone for our team, offering a powerful, computationally efficient alternative to traditional MCMC methods for researchers around the globe.
Dr. Krainski and Dr. Esmail Lead INLA Training and Showcase Novel Graph-Based Correlation Models in Montpellier 1 min read · Sun, Apr 19 2026 News INLA The BAYESCOMP research group continues to amplify its global scientific footprint, demonstrating leadership in advanced statistical computing and Bayesian inference through a highly successful recent visit to Montpellier.
Dr. Elias Krainski Leads INLA Course in Brazil 1 min read · Mon, Mar 30 2026 Spotlight R-INLA INLA Bayesian and computational Statistics Demonstrating our research group’s ongoing commitment to international collaboration and knowledge sharing, Dr. Elias Krainski recently concluded a successful five-day online short course on advanced statistical modelling for two premier Brazilian institutions: Universidade Federal de Minas Gerais (UFMG) and Universidade Federal do Paraná (UFPR).
INLA course at UPNA 1 min read · Tue, Jan 31 2023 News Prof. Haavard Rue and Dr. Elias Krainski presented a course on Bayesian spatial and spatio-temporal modeling with R-INLA at the Public University of Navarre (UPNA) in Pamplona, Spain during 25-27 January 2023.
INLA workshop at University of Glasgow 1 min read · Tue, Jan 31 2023 News Prof. Haavard Rue and Dr. Elias Krainski presented a workshop for INLA users and developers titled "INLA: past, present and future" at the University of Glasgow during 18-19 January.
INLA course at Bordeaux population health center 1 min read · Wed, Jun 15 2022 News Some members of the INLA team presented an INLA short course to the Biostatistics group at the Bordeaux population health center of INSERM and the University of Bordeaux. The content was tailored for biostatistics and public health applications, to avail INLA as a tool for fast Bayesian inference of applicable statistical models.
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