Posters (titles + links to PDFs)

Areti Boulieri (Imperial College London): Disease surveillance on asthma using BaySTDetect

Maurizio Filippone (University of Glasgow): Scalable stochastic gradient-based inference for Gaussian processes

Óli Páll Geirsson (University of Iceland): The MCMC split sampler: A block Gibbs sampling scheme for latent Gaussian models

Virgilio Gomez-Rubio (Universidad de Castilla-La Mancha): Extending the Integrated Laplace Approximation

Virgilio Gomez-Rubio (Universidad de Castilla-La Mancha): A New Latent Class to Fit Spatial Econometrics Models with Integrated Nested Laplace Approximations

Anna Heath (University College London): Efficient High-Dimensional Gaussian Process Regression to calculate the Expected Value of Partial Perfect Information in Health Economic Evaluations using R-INLA

Morten Holm Falk (NTNU): A Robust Bayesian Gaussian Analysis

Øyvind Hoveid (NILF): Relatively informative priors and Watanabe's information criterion in latent Gaussian models

Birgir Hrafnkelsson (University of Iceland): Bayesian flood frequency analysis using monthly maxima

Thomas Jagger (Florida State University): A Statistical Framework for Regional Tornado Climatology

Alex Karagiannis (Swiss TPH): Bayesian variable selection of spatiotemporally varying coefficients with predictive processes using i-INLA

Nadja Klein (University of Göttingen): Scale-Dependent Priors for Variance Parameters in Structured Additive Distributional Regression

Amanda Lenzi (Technical University of Denmark): Statistical Modelling of Spatial process with application in Renewable Energy

Mattia Molinaro (University of Zurich): A bivariate Gaussian Markov Random Field over large grids

Helen Ogden (University of Warwick): Exploiting the graphical structure of latent Gaussian models

Oscar Rodríguez de Rivera (Imperial College London): SPDE in species distribution

Rafael Sauter (University of Zurich): Network meta-analysis with integrated nested Laplace approximations

Massimo Ventrucci (University of Bologna): Penalized Complexity priors for degrees of freedom in P-spline models