Gaussian predictive process models for large spatial data sets. - Wikidata - wikimedia - TriplyDB

Gaussian predictive process models for large spatial data sets.

Gaussian predictive process models for large spatial data sets.

http://www.wikidata.org/entity/Q33502739

about

Individualized Gaussian process-based prediction and detection of local and global gray matter abnormalities in elderly subjects Imputation of confidential data sets with spatial locations using disease mapping models MULTIPLE IMPUTATION FOR SHARING PRECISE GEOGRAPHIES IN PUBLIC USE DATA Geostatistical model-based estimates of Schistosomiasis prevalence among individuals aged ≤ 20 years in West Africa.Estimating causal effects of air quality regulations using principal stratification for spatially correlated multivariate intermediate outcomes Hierarchical factor models for large spatially misaligned data: a low-rank predictive process approach.Hierarchical Modeling for Spatial Data Problems SGPP: spatial Gaussian predictive process models for neuroimaging data Bayesian analysis of zero inflated spatiotemporal HIV/TB child mortality data through the INLA and SPDE approaches: Applied to data observed between 1992 and 2010 in rural North East South Africa Relationship between child survival and malaria transmission: an analysis of the malaria transmission intensity and mortality burden across Africa (MTIMBA) project data in Rufiji demographic surveillance system, Tanzania.Modelling heterogeneity in malaria transmission using large sparse spatio-temporal entomological data.A Bayesian nonparametric model for spatially distributed multivariate binary data with application to a multidrug-resistant tuberculosis (MDR-TB) study BAYESIAN SPATIAL-TEMPORAL MODELING OF ECOLOGICAL ZERO-INFLATED COUNT DATA.HIERARCHICAL SPATIAL MODELS FOR PREDICTING TREE SPECIES ASSEMBLAGES ACROSS LARGE DOMAINS ANALYSIS OF MINNESOTA COLON AND RECTUM CANCER POINT PATTERNS WITH SPATIAL AND NONSPATIAL COVARIATE INFORMATION Hierarchical Spatial Process Models for Multiple Traits in Large Genetic Trials Space-time data fusion under error in computer model output: an application to modeling air quality A Flexible Spatio-Temporal Model for Air Pollution with Spatial and Spatio-Temporal Covariates.ANALYSIS OF MULTIPLE SCLEROSIS LESIONS VIA SPATIALLY VARYING COEFFICIENTS.Large-scale drivers of malaria and priority areas for prevention and control in the Brazilian Amazon region using a novel multi-pathogen geospatial model Spatio-temporal distribution of soil-transmitted helminth infections in Brazil.Bayesian geostatistical modeling of leishmaniasis incidence in Brazil.A Hierarchical Model for Quantifying Forest Variables Over Large Heterogeneous Landscapes With Uncertain Forest Areas The basis function approach for modeling autocorrelation in ecological data.Efficient Bayesian hierarchical functional data analysis with basis function approximations using Gaussian-Wishart processes.Reduced-Rank Spatio-Temporal Modeling of Air Pollution Concentrations in the Multi-Ethnic Study of Atherosclerosis and Air Pollution.A HIERARCHICAL MAX-STABLE SPATIAL MODEL FOR EXTREME PRECIPITATION.Efficient Gaussian process regression for large datasets.Advances in mapping malaria for elimination: fine resolution modelling of Plasmodium falciparum incidence.Time-space Kriging to address the spatiotemporal misalignment in the large datasets.On the Effect of Preferential Sampling in Spatial Prediction.Improving the performance of predictive process modeling for large datasets.Hierarchical spatial modeling of additive and dominance genetic variance for large spatial trial datasets.Bayesian wombling for spatial point processes.Extending distributed lag models to higher degrees.Modeling nonstationarity in space and time.A rigorous statistical framework for spatio-temporal pollution prediction and estimation of its long-term impact on health.A Dynamic Bayesian Model for Characterizing Cross-Neuronal Interactions During Decision-Making.Space-time investigation of the effects of fishing on fish populations.Adaptive Gaussian Predictive Process Models for Large Spatial Datasets.

P2860

Q28656446-FCD41D13-683D-4471-9492-F76989AF3820 Q28658063-C577F222-78D7-4431-99F2-A2DC299D6BC3 Q28681429-8CB850BB-D8BA-4053-9AFC-AFCEE0E1B16D Q30485053-B86C28AB-C2B6-47C1-8CF3-94922CB2557D Q30508481-BD5D4B17-32C6-431F-9DC7-4450922F4647 Q30586935-EA429B2C-FE22-4DCE-9C61-DC3A04E019FE Q30665311-9E0D9E86-E86F-46B3-9298-6E0B52252865 Q30700256-958B565E-249E-41D4-8DDD-728D843114D7 Q30747079-122B3401-2866-4312-A8E9-31B1B6D0C28D Q30788258-14BC87A4-5714-4724-9CF1-943ED21704B7 Q30833356-E4A7E27E-5D2A-41C7-A9B3-94468FBB2A76 Q30833918-95A0BDC4-1138-4D39-A332-678D9058575A Q31061171-687F81D4-797E-45A3-9345-511852FE6E95 Q33547402-5FC9653A-CDAB-4172-8267-10F43B9B077C Q33803576-8AAFABED-F944-41FA-BE62-DE1FBA6053A4 Q34027217-5781C203-8A28-42BB-94EB-B547FD975C12 Q34115505-B075515D-488D-41FD-87E5-F8A37F2D28DE Q34241308-3898C39B-3A6A-45F6-9D0E-60F4728CACC6 Q34571240-561C0B1B-1A87-42B9-B720-D8FDC2475445 Q34586518-F38F0305-8C77-4FD1-9369-0535996D9F21 Q34670955-70DB3BD8-E71A-42AF-95CB-FA523DB6C540 Q34723446-C4A2DFFB-6D27-47EC-BD59-16AC944D33C7 Q35797567-74ACE070-F2F7-4643-93ED-8B66637D2853 Q36217435-AF24D3C3-3DFB-40A8-BA0A-79F1E4AD52D3 Q36342354-32ADB9D2-D851-48E3-B860-F64A4A4E8891 Q36716645-06C81056-D1BB-4652-9053-10E3B3F12417 Q36945913-FF490A03-CB8C-4C08-9566-61EC510A6AE1 Q37017350-1ADA453B-5A8F-4318-8F96-118B11C34DEE Q37089513-EE98865F-663A-463D-8CE5-07E55355378D Q37158346-559242A6-A600-4010-97E3-FBE9CE927E48 Q37198216-0DD5CE9A-5DAB-4FC4-BA46-78AB8816D459 Q37344820-FE232ADA-45B9-47C2-8F35-D849E1FA4376 Q37416095-1A4795C7-2AEA-4D2E-B17B-B15CC558A242 Q37480745-7320F9D7-34F7-4454-9AE5-A2207669E7BF Q37621897-AA23AA95-D10F-416F-801F-95CE2354E0B5 Q38992178-8625A9D9-BDCD-402F-973C-8D577EE869A2 Q39063482-BE87A65F-3E29-457C-8941-183E1B051E42 Q39090435-83A0A26B-257D-49AB-9E3A-82C0C82FF74A Q39746210-C784348F-8B98-4199-A2B4-B041995619A0 Q40719991-DCF59974-DC4F-4CDA-AE46-D40B5D339A06

P2860

Gaussian predictive process models for large spatial data sets.

http://www.wikidata.org/entity/Q33502739

description

2008 nî lūn-bûn

@nan

2008 թուականի Սեպտեմբերին հրատարակուած գիտական յօդուած

@hyw

2008 թվականի սեպտեմբերին հրատարակված գիտական հոդված

@hy

2008年の論文

@ja

2008年論文

@yue

2008年論文

@zh-hant

2008年論文

@zh-hk

2008年論文

@zh-mo

2008年論文

@zh-tw

2008年论文

@wuu

name

Gaussian predictive process models for large spatial data sets.

@ast

Gaussian predictive process models for large spatial data sets.

@en

type

label

Gaussian predictive process models for large spatial data sets.

@ast

Gaussian predictive process models for large spatial data sets.

@en

prefLabel

Gaussian predictive process models for large spatial data sets.

@ast

Gaussian predictive process models for large spatial data sets.

@en

P1433

Q33502739-527F3E9A-BB5E-4A37-ADBF-5FBF3F9F4CE4

P1476

Q33502739-BED38E16-DCE1-4C0F-9C41-8A763AFCB1FE

P2093

Q33502739-6D260599-C781-4AEB-AC20-9496B6D9D05E

Q33502739-7B961531-1E1F-4CCF-AB83-DC52E32F5C17

Q33502739-8C0FD013-0C1F-403D-B972-9C44ED6893AF

Q33502739-C0B7B886-D200-4E6E-974D-80CEE9E888D0

P2860

Q33502739-46F615D3-EEC4-4689-912E-A45BFF772288

Q33502739-8C0337F2-E86D-448E-A0DF-F9FC716F5BD2

Q33502739-96F1C6C9-E256-4837-A04D-0CBE2F0D3072

Q33502739-C06DE11F-EA51-4FE4-B5C5-6A5AD0CA6714

P304

Q33502739-98635595-4EE7-4AB3-80DB-988C690E7D13

P31

Q33502739-0A25B783-E29A-4CC2-8024-2449F7704C9B

P356

Q33502739-65B68877-B72B-4682-8B43-E5EE00455A8E

P433

Q33502739-79C8807A-47B8-435C-969D-A86EE48360B4

P478

Q33502739-A7F652F0-2D9A-49AE-AD35-B993711D4DBD

P577

Q33502739-37FEECAF-7FED-473F-8B52-9EB5B5847092

P5875

Q33502739-2B040CB2-577B-4B04-8C9F-105BF4CDDCB3

P698

Q33502739-3C083B02-49D3-4866-BC05-1C76A873AF18

P932

Q33502739-349CDF0E-B9C0-4625-8FD4-D594F677643A

P356

J.1467-9868.2008.00663.X

P1433

Journal of the Royal Statistical Society Series B: Statistical Methodology

P1476

Gaussian predictive process models for large spatial data sets.

@en

P2093

Alan E Gelfand

http://www.w3.org/2001/XMLSchema#string

Andrew O Finley

http://www.w3.org/2001/XMLSchema#string

Huiyan Sang

http://www.w3.org/2001/XMLSchema#string

Sudipto Banerjee

http://www.w3.org/2001/XMLSchema#string

P2860

Approximate likelihood for large irregularly spaced spatial data

Spatial Modelling Using a New Class of Nonstationary Covariance Functions.

Computational Techniques for Spatial Logistic Regression with Large Datasets

Spline Models for Observational Data

P304

825-848

http://www.w3.org/2001/XMLSchema#string

P31

scholarly article

P356

10.1111/J.1467-9868.2008.00663.X

http://www.w3.org/2001/XMLSchema#string

P433

4

http://www.w3.org/2001/XMLSchema#string

P478

70

http://www.w3.org/2001/XMLSchema#string

P577

2008-09-01T00:00:00Z

http://www.w3.org/2001/XMLSchema#dateTime

P5875

26808285

http://www.w3.org/2001/XMLSchema#string

P698

19750209

http://www.w3.org/2001/XMLSchema#string

P932

2741335

http://www.w3.org/2001/XMLSchema#string