ListarMAT_Articles por tema "Bayesian inference"
Mostrando ítems 1-8 de 8
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A Bayesian machine learning approach for spatio-temporal prediction of COVID-19 cases
Springer (2022)Modeling the spread of infectious diseases in space and time needs to take care of complex dependencies and uncertainties. Machine learning methods, and neural networks, in particular, are useful in modeling this sort of ... -
A New Bayesian Inference Methodology for Modeling Geochemical Elements in Soil with Covariates. Characterization of Lithium in South Iberian Range (Spain)
Global Journals Inc. (2016)When the scientific need to model geochemical elements in soil, is using geostatistical methodologies, for instance krigings, but we can use a new possibility with Bayesian Inference. The ... -
A Spatially Correlated Model with Generalized Autoregressive Conditionally Heteroskedastic Structure for Counts of Crimes
MDPI (2022-06-29)Crime is a negative phenomenon that affects the daily life of the population and its development. When modeling crime data, assumptions on either the spatial or the temporal relationship between observations are necessary ... -
A spatio-temporal multinomial model of firearm death in Ecuador
Elsevier (2023)This paper presents a statistical model based on a multinomial distribution with fixed and random effects, the latter effects being structured and non-structured in space and time. Inference is performed through a Bayesian ... -
Enhancing the SPDE modeling of spatial point processes with INLA, applied to wildfires. Choosing the best mesh for each database
Taylor & Francis (2019)Wildfires play an important role in shaping landscapes and as a source of CO2 and particulate matter, and are a typical spatial point process studied in many papers. Modeling the spatial variability of a wildfire could be ... -
Modeling fire size of wildfires in Castellon (Spain), using spatiotemporal marked point processes
Elsevier (2016-12-01)The extent of fires, their periodicity and their impact on terrestrial communities have been a major con- cern in the last century. Wildfires play an important role in shaping landscapes and as a source of CO 2 and particulate ... -
Point process modeling through a mixture of homogeneous and self-exciting processes
Wiley (2024)Self-exciting point processes allow modeling thetemporal location of an event of interest, consideringthe history provided by previously observed events.This family of point processes is commonly used inseveral areas such ... -
Spatio-temporal hierarchical Bayesian analysis of wildfires with Stochastic Partial Differential Equations. A case study from Valencian Community (Spain)
Taylor & Francis (2019-09-04)The spatio-temporal study of wildfires has two complex elements that are the computational efficiency and longtime processing. Modelling the spatial variability of a wildfire could be performed in different ways, and an ...