{"data":{"id":"10.6084/m9.figshare.1627942","type":"dois","attributes":{"doi":"10.6084/m9.figshare.1627942","prefix":"10.6084","suffix":"m9.figshare.1627942","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"Cseke, Botond","nameType":"Personal","givenName":"Botond","familyName":"Cseke","affiliation":[],"nameIdentifiers":[]},{"name":"Zammit-Mangion, Andrew","nameType":"Personal","givenName":"Andrew","familyName":"Zammit-Mangion","affiliation":[],"nameIdentifiers":[]},{"name":"Heskes, Tom","nameType":"Personal","givenName":"Tom","familyName":"Heskes","affiliation":[],"nameIdentifiers":[]},{"name":"Sanguinetti, Guido","nameType":"Personal","givenName":"Guido","familyName":"Sanguinetti","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"Sparse Approximate Inference for Spatio-Temporal Point Process Models"}],"publisher":"Taylor \u0026 Francis","container":{},"publicationYear":2015,"subjects":[{"subject":"Space Science"},{"subject":"69999 Biological Sciences not elsewhere classified","subjectScheme":"FOR"},{"subject":"FOS: Biological sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Biological sciences","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"80699 Information Systems not elsewhere classified","subjectScheme":"FOR"},{"subject":"FOS: Computer and information sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Computer and information sciences","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"19999 Mathematical Sciences not elsewhere classified","subjectScheme":"FOR"},{"subject":"FOS: Mathematics","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Mathematics","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Science Policy"},{"subject":"Computational Biology"}],"contributors":[],"dates":[{"date":"2015-12-23","dateType":"Created"},{"date":"2019-10-25","dateType":"Updated"},{"date":"2015","dateType":"Issued"}],"language":null,"types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"Dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsSupplementTo","relatedIdentifier":"10.1080/01621459.2015.1115357","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["554192 Bytes"],"formats":[],"version":null,"rightsList":[{"rights":"Creative Commons Attribution 4.0 International","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"Spatio-temporal log-Gaussian Cox process models play a central role in the analysis of spatially distributed systems in several disciplines. Yet, scalable inference remains computationally challenging both due to the high-resolution modeling generally required and the analytically intractable likelihood function. Here, we exploit the sparsity structure typical of (spatially) discretized log-Gaussian Cox process models by using approximate message-passing algorithms. The proposed algorithms scale well with the state dimension and the length of the temporal horizon with moderate loss in distributional accuracy. They hence provide a flexible and faster alternative to both nonlinear filtering-smoothing type algorithms and to approaches that implement the Laplace method or expectation propagation on (block) sparse latent Gaussian models. We infer the parameters of the latent Gaussian model using a structured variational Bayes approach. We demonstrate the proposed framework on simulation studies with both Gaussian and point-process observations and use it to reconstruct the conflict intensity and dynamics in Afghanistan from the WikiLeaks Afghan War Diary. Supplementary materials for this article are available online.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"xml":"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","url":"https://tandf.figshare.com/articles/dataset/Sparse_Approximate_Inference_for_Spatio_Temporal_Point_Process_Models/1627942/1","contentUrl":null,"metadataVersion":44,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":1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