Aproximações de distribuições marginais a posteriori utilizando o método INLA [PDF]
O uso da Estatística Bayesiana vem se tornando cada vez mais frequente, isso pois os modelos são mais intuitivos e em alguns casos apenas modelos Bayesianos obtêm sucesso (modelos de Teoria da Resposta ao Item por exemplo). O grande problema surge quando
Azevedo, Douglas Roberto Mesquita
core
A primer on disease mapping and ecological regression using $${\texttt{INLA}}$$ [PDF]
Spatial and spatio-temporal disease mapping models are widely used for the analysis of registry data and usually formulated in a hierarchical Bayesian framework. Explanatory variables can be included by a so-called ecological regression.
Held, Leonhard, Schrödle, Birgit
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A Bayesian regression framework for circular models with INLA
Regression models for circular variables are less developed, since the concept of building a linear predictor from linear combinations of covariates and various random effects, breaks the circular nature of the variable. In this paper, we introduce a new approach to rectify this issue, leading to well-defined regression models for circular responses ...
Ye, Xiang +2 more
openaire +2 more sources
Workshop on Spatial Modelling with INLA [PDF]
openaire +1 more source
Advancing multilevel Bayesian networks with efficient Bayesian inference. [PDF]
Yirdaw BE +3 more
europepmc +1 more source
Clustering and Source Association of Clinical and Nonclinical Listeria monocytogenes Isolates, New York, USA, 2000-2021<sup>1</sup>. [PDF]
Samut H +12 more
europepmc +1 more source
A Fast, Flexible Simulation Framework for Bayesian Adaptive Designs-The R Package BATSS. [PDF]
Couturier DL +4 more
europepmc +1 more source
Protocol for spatiotemporal analysis of climate-induced negative sentiment in social media. [PDF]
Al-Ahdal T +3 more
europepmc +1 more source
A Bayesian spline-augmented piecewise exponential model with spatial frailty for under-five mortality in Nigeria. [PDF]
Omaku PE, Braimah JO, Correa FM.
europepmc +1 more source
Distributed inference with INLA
Distributed inference is often used to reduce computational costs and memory usage, but it is also essential in cases where processing all the data jointly is not feasible.
Figueira, Mario
core

