Spatial Statistical Models: An Overview under the Bayesian Approach
Spatial documentation is exponentially increasing given the availability of <i>Big Data in the Internet of Things</i>, enabled by device miniaturization and data storage capacity. Bayesian spatial statistics is a useful statistical tool to determine the dependence structure and hidden pa...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-11-01
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| Series: | Axioms |
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| Online Access: | https://www.mdpi.com/2075-1680/10/4/307 |
| _version_ | 1797506627504439296 |
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| author | Francisco Louzada Diego Carvalho do Nascimento Osafu Augustine Egbon |
| author_facet | Francisco Louzada Diego Carvalho do Nascimento Osafu Augustine Egbon |
| author_sort | Francisco Louzada |
| collection | DOAJ |
| description | Spatial documentation is exponentially increasing given the availability of <i>Big Data in the Internet of Things</i>, enabled by device miniaturization and data storage capacity. Bayesian spatial statistics is a useful statistical tool to determine the dependence structure and hidden patterns in space through prior knowledge and data likelihood. However, this class of modeling is not yet well explored when compared to adopting classification and regression in machine-learning models, in which the assumption of the spatiotemporal independence of the data is often made, that is an inexistent or very weak dependence. Thus, this systematic review aims to address the main models presented in the literature over the past 20 years, identifying the gaps and research opportunities. Elements such as random fields, spatial domains, prior specification, the covariance function, and numerical approximations are discussed. This work explores the two subclasses of spatial smoothing: <i>global</i> and <i>local</i>. |
| first_indexed | 2024-03-10T04:35:14Z |
| format | Article |
| id | doaj.art-f601a63b39dd4d7f807a5aaefcfb5a3e |
| institution | Directory Open Access Journal |
| issn | 2075-1680 |
| language | English |
| last_indexed | 2024-03-10T04:35:14Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Axioms |
| spelling | doaj.art-f601a63b39dd4d7f807a5aaefcfb5a3e2023-11-23T03:49:51ZengMDPI AGAxioms2075-16802021-11-0110430710.3390/axioms10040307Spatial Statistical Models: An Overview under the Bayesian ApproachFrancisco Louzada0Diego Carvalho do Nascimento1Osafu Augustine Egbon2Institute of Mathematical Science and Computing, University of Sao Paulo, Sao Carlos 13566-590, BrazilDepartamento de Matemática, Facultad de Ingeniería, Universidad de Atacama, Copiapó 1530000, ChileInstitute of Mathematical Science and Computing, University of Sao Paulo, Sao Carlos 13566-590, BrazilSpatial documentation is exponentially increasing given the availability of <i>Big Data in the Internet of Things</i>, enabled by device miniaturization and data storage capacity. Bayesian spatial statistics is a useful statistical tool to determine the dependence structure and hidden patterns in space through prior knowledge and data likelihood. However, this class of modeling is not yet well explored when compared to adopting classification and regression in machine-learning models, in which the assumption of the spatiotemporal independence of the data is often made, that is an inexistent or very weak dependence. Thus, this systematic review aims to address the main models presented in the literature over the past 20 years, identifying the gaps and research opportunities. Elements such as random fields, spatial domains, prior specification, the covariance function, and numerical approximations are discussed. This work explores the two subclasses of spatial smoothing: <i>global</i> and <i>local</i>.https://www.mdpi.com/2075-1680/10/4/307Bayesian spatial modelsBayesian inferenceprobability and statistical methods |
| spellingShingle | Francisco Louzada Diego Carvalho do Nascimento Osafu Augustine Egbon Spatial Statistical Models: An Overview under the Bayesian Approach Axioms Bayesian spatial models Bayesian inference probability and statistical methods |
| title | Spatial Statistical Models: An Overview under the Bayesian Approach |
| title_full | Spatial Statistical Models: An Overview under the Bayesian Approach |
| title_fullStr | Spatial Statistical Models: An Overview under the Bayesian Approach |
| title_full_unstemmed | Spatial Statistical Models: An Overview under the Bayesian Approach |
| title_short | Spatial Statistical Models: An Overview under the Bayesian Approach |
| title_sort | spatial statistical models an overview under the bayesian approach |
| topic | Bayesian spatial models Bayesian inference probability and statistical methods |
| url | https://www.mdpi.com/2075-1680/10/4/307 |
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