about
Imputation of confidential data sets with spatial locations using disease mapping modelsObject oriented data analysis under spatial correlation.Common-input models for multiple neural spike-train data.Bayesian prediction of spatial count data using generalized linear mixed models.Bayesian partitioning for modeling and mapping spatial case-control data.Functional Principal Component Analysis of Spatio-Temporal Point Processes with Applications in Disease Surveillance.Quasi-likelihood for Spatial Point Processes.A Statistical Model for In Vivo Neuronal Dynamics.Spatial Bayesian latent factor regression modeling of coordinate-based meta-analysis data.Decomposition of Variance for Spatial Cox ProcessesBayesian geostatistical modelling with informative sampling locations.Disentangling the effects of geographic and ecological isolation on genetic differentiation.Bayesian inference for smoking cessation with a latent cure state.Assessing Cyanobacterial Harmful Algal Blooms as Risk Factors for Amyotrophic Lateral Sclerosis.Second-order quasi-likelihood for spatial point processes.Bias-corrected variance estimation and hypothesis testing for spatial point and marked point processes using subsampling.Scaling properties of urban facilities.Point pattern analysis with spatially varying covariate effects, applied to the study of cerebrovascular deaths.Spatial ecology of bacteria at the microscale in soil.Second-order analysis of semiparametric recurrent event processes.Gaussian process-based Bayesian nonparametric inference of population size trajectories from gene genealogies.Multivariate product-shot-noise Cox point process models.Bayesian Computation for Log-Gaussian Cox Processes: A Comparative Analysis of Methods.Approximate inference for disease mapping with sparse Gaussian processes.A non-stationary spatial generalized linear mixed model approach for studying plant diversityFitting complex ecological point process models with integrated nested Laplace approximationBayesian hierarchical models for analysing spatial point-based data at a grid level: a comparison of approachesRecent Bayesian approaches for spatial analysis of 2-D images with application to environmental modellingPoint process models for presence-only analysisGoing off grid: computationally efficient inference for log-Gaussian Cox processesHIERARCHICAL SPATIAL MODELS OF ABUNDANCE AND OCCURRENCE FROM IMPERFECT SURVEY DATAGeneralised additive point process models for natural hazard occurrenceEstimating multispecies abundance using automated detection systems: ice-associated seals in the Bering SeaAnalysis of multispecies point patterns by using multivariate log-Gaussian Cox processesINLA or MCMC? A tutorial and comparative evaluation for spatial prediction in log-Gaussian Cox processesBayesian Estimation and Prediction for Inhomogeneous Spatiotemporal Log-Gaussian Cox Processes Using Low-Rank Models, With Application to Criminal SurveillanceSpatial simulation and modelling of the early Pleistocene site of DS (Bed I, Olduvai Gorge, Tanzania): a powerful tool for predicting potential archaeological information from unexcavated areas
P2860
Q28658063-08204964-B953-4EC1-8A38-3EE6DA92D2C6Q30755570-43149802-4ACB-456A-A84D-87BAE94CC3E5Q31133418-62D8A67C-C5E1-4901-BA63-6A399081867DQ32135627-8F198557-25E8-46A8-9C8A-F5ACF0603D44Q33408338-47BE1DAB-7FB7-4F3A-B3EB-88F23BB9F52DQ34432505-B6120293-61C3-4BDE-A6E2-8E762DAF3E05Q35668499-444D674F-33FA-43B3-AF29-A76548394BC9Q35842188-2EA2EF3A-63B6-4E74-956B-A858C96E6650Q36370163-3C8A2A6D-5063-4BF1-93FC-5FA0598BFB0CQ36765843-770BBE08-AA90-44F8-95C7-B9119A56E75EQ37097894-DD5310B4-D424-49F1-9D91-BC5B9BF9BEEBQ37255768-E721328A-BE79-49DA-98AC-82FF811334F4Q37376787-BEA7B27D-8E02-4B79-9A64-B80E6FC26981Q38805620-DCF887BC-189A-478E-9851-377111A2F261Q38859937-2E66016B-F039-4E93-B547-D15991E2FED8Q39035071-C6EA8DDE-128D-448C-9B76-A14ECC995A77Q40251860-DD813FC8-11BB-4E46-BAE3-7277BBEA45C0Q41656502-4895B07F-BD3C-4622-BF3E-5CEB4B2CF870Q41896790-A0BE95F4-12FF-4BA3-AFC7-29A5698F6F88Q42819329-DA869CDB-60B7-458C-9648-EE2511E34201Q42989213-EC4BF594-400E-45F9-91A1-DBEFD9540A7AQ46710968-D118861A-C19F-4959-88FE-2CFF77A6DFE9Q47375882-DB12DEA7-7712-4C30-A7B1-3BC76539F176Q51049905-A917B261-8FEE-47C3-BFF7-4FB2BB3DCC96Q56755086-D16CAF79-C32A-4272-899A-901C01CFDD05Q56952684-0EC86D2F-674B-4012-A4AB-8B2083F0F244Q56994057-D30EF51F-8E63-470E-BBA5-F2E3F7F9ABFEQ56994099-844E4964-D728-4204-834A-6EC62FAE2767Q57062642-A86420F8-23DD-4663-86D5-AAE65F7D95E7Q57266346-EDBCB3DA-78AC-4BE8-A3F9-5D4F547552B2Q57878141-B4C96489-6DD2-4E1D-87BD-880D8A4507C5Q57916316-DFE6CBBC-72DE-47DA-BAFB-E417FE7F9C89Q58055873-4BD5C84D-795A-486F-A2F6-F84D66B8235DQ58285574-9C55C527-4AED-458F-AC23-10B454165EB8Q58851770-3D47525E-AFCA-42D9-B276-5515B5BA7164Q58851859-B655F05B-4B4A-4467-B0E9-59F8132D064CQ59269330-45482B85-34A7-4B7F-A0C6-E96C8979D990
P2860
description
wetenschappelijk artikel
@nl
наукова стаття, опублікована у вересні 1998
@uk
name
Log Gaussian Cox Processes
@en
Log Gaussian Cox Processes
@nl
type
label
Log Gaussian Cox Processes
@en
Log Gaussian Cox Processes
@nl
prefLabel
Log Gaussian Cox Processes
@en
Log Gaussian Cox Processes
@nl
P2093
P356
P1476
Log Gaussian Cox Processes
@en
P2093
Anne Randi Syversveen
Jesper Moller
Rasmus Plenge Waagepetersen
P304
P356
10.1111/1467-9469.00115
P577
1998-09-01T00:00:00Z