Mathematics, 2023 Geostatistics data in regions always have highly spatial heterogeneous, yet the regional features of the data itself cannot be ignored. In this paper, a novel latent Bayesian spatial model is proposed, which incorporates the spatial dependence of ...
Zongyuan Xia +4 more
doaj +1 more source
Computer model calibration with large non-stationary spatial outputs: application to the calibration of a climate model [PDF]
, 2018 Bayesian calibration of computer models tunes unknown input parameters by comparing outputs with observations. For model outputs that are distributed over space, this becomes computationally expensive because of the output size.
Alexander M. J. +6 more
core +2 more sources
Incorporating Biotic Information in Species Distribution Models: A Coregionalized Approach
Mathematics, 2021 In this work, we discuss the use of a methodological approach for modelling spatial relationships among species by means of a Bayesian spatial coregionalized model.
Xavier Barber +5 more
doaj +1 more source
Parallelized integrated nested Laplace approximations for fast Bayesian inference
Statistics and Computing, 2022 There is a growing demand for performing larger-scale Bayesian inference tasks, arising from greater data availability and higher-dimensional model parameter spaces. In this work we present parallelization strategies for the methodology of integrated nested Laplace approximations (INLA), a popular framework for performing approximate Bayesian inference
Lisa Gaedke-Merzhäuser +3 more
openaire +4 more sources
An Introduction to Predictive Processing Models of Perception and Decision‐Making
Topics in Cognitive Science, EarlyView., 2023 Abstract The predictive processing framework includes a broad set of ideas, which might be articulated and developed in a variety of ways, concerning how the brain may leverage predictive models when implementing perception, cognition, decision‐making, and motor control.
Mark Sprevak, Ryan Smith
wiley +1 more source
Bayesian Analysis of Population Health Data
Mathematics, 2021 The analysis of population-wide datasets can provide insight on the health status of large populations so that public health officials can make data-driven decisions. The analysis of such datasets often requires highly parameterized models with different
Dorota Młynarczyk +3 more
doaj +1 more source
INLA or MCMC? A Tutorial and Comparative Evaluation for Spatial Prediction in log-Gaussian Cox Processes [PDF]
, 2012 We investigate two options for performing Bayesian inference on spatial log-Gaussian Cox processes assuming a spatially continuous latent field: Markov chain Monte Carlo (MCMC) and the integrated nested Laplace approximation (INLA). We first describe the
Diggle, Peter J., Taylor, Benjamin M.
core +1 more source
A toolbox for fitting complex spatial point process models using integrated nested Laplace approximation (INLA) [PDF]
, 2012 "The authors also gratefully acknowledge the financial support of Research Councils UK for Illian"
J. Illian, S. Sørbye, H. Rue
semanticscholar +1 more source
EpiMix: A novel method to estimate effective reproduction number
Infectious Disease Modelling, 2023 Transmission potential of a pathogen, often quantified by the time-varying reproduction number Rt, provides the current pace of infection for a disease and indicates whether an emerging epidemic is under control. In this study, we proposed a novel method,
Shihui Jin +3 more
doaj +1 more source
Bayesian Computing with INLA: A Review [PDF]
, 2016 The key operation in Bayesian inference is to compute high-dimensional integrals. An old approximate technique is the Laplace method or approximation, which dates back to Pierre-Simon Laplace (1774).
Bolin D +8 more
core +2 more sources