2020 - Fellow of the American Association for the Advancement of Science (AAAS)
2012 - Fellow of the American Statistical Association (ASA)
Sudipto Banerjee focuses on Econometrics, Markov chain Monte Carlo, Spatial analysis, Data mining and Multivariate statistics. The Econometrics study combines topics in areas such as Survival analysis, Inference, Geographic coordinate system and Linear model. His Markov chain Monte Carlo study incorporates themes from Hierarchical database model and Bayesian inference.
His Bayesian inference research incorporates elements of Machine learning and Univariate. His work deals with themes such as Marginal model, Computation, Markov chain and Joint probability distribution, which intersect with Spatial analysis. His Multivariate statistics study necessitates a more in-depth grasp of Statistics.
Sudipto Banerjee mainly investigates Bayesian probability, Bayesian inference, Data mining, Gaussian process and Spatial analysis. The concepts of his Bayesian probability study are interwoven with issues in Machine learning, Econometrics and Data science. The various areas that Sudipto Banerjee examines in his Bayesian inference study include Inference, Spatial dependence and Markov chain Monte Carlo.
His Markov chain Monte Carlo research includes elements of Spatial variability, Markov chain, Geocoding and Statistical model. His Data mining research is multidisciplinary, incorporating perspectives in Directed acyclic graph and Prior probability. His Spatial analysis research includes themes of Univariate, Multivariate statistics, Hierarchical database model, Statistical inference and Autoregressive model.
His primary scientific interests are in Spatial analysis, Bayesian inference, Gaussian process, Bayesian probability and Inference. His Spatial analysis research is multidisciplinary, relying on both Univariate, Multivariate statistics, Hierarchical database model, Applied mathematics and Spatial ecology. He has included themes like Data mining, Statistical inference, State space, Sampling and Spatial dependence in his Bayesian inference study.
His Bayesian probability research incorporates themes from Physical model and Econometrics. He has researched Inference in several fields, including Data science, Linear model, Kriging and Markov chain Monte Carlo. His Reduction study integrates concerns from other disciplines, such as Machine learning and Artificial intelligence.
Sudipto Banerjee mainly focuses on Gaussian process, Algorithm, Bayesian probability, k-nearest neighbors algorithm and Spatial analysis. His Algorithm research integrates issues from Univariate and Multivariate normal distribution. His research in the fields of Bayesian inference overlaps with other disciplines such as Scalability.
His research integrates issues of Linear model and Data mining in his study of Bayesian inference. His studies in k-nearest neighbors algorithm integrate themes in fields like Efficient algorithm, Stochastic process, Computational statistics and Spatial regression model. His Spatial analysis study combines topics from a wide range of disciplines, such as Forest management, Lidar, Ranging and Geographic information system.
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Hierarchical Modeling and Analysis for Spatial Data
Sudipto Banerjee;Bradley P. Carlin;Alan E. Gelfand.
Gaussian predictive process models for large spatial data sets
Sudipto Banerjee;Alan E. Gelfand;Andrew O. Finley;Huiyan Sang.
Journal of The Royal Statistical Society Series B-statistical Methodology (2008)
Spatial Modeling With Spatially Varying Coefficient Processes
Alan E Gelfand;Hyon-Jung Kim;C. F Sirmans;Sudipto Banerjee.
Journal of the American Statistical Association (2003)
Hierarchical Nearest-Neighbor Gaussian Process Models for Large Geostatistical Datasets.
Abhirup Datta;Sudipto Banerjee;Andrew O. Finley;Alan E. Gelfand.
Journal of the American Statistical Association (2016)
Nonstationary Multivariate Process Modeling through Spatially Varying Coregionalization
Alexandra M. Schmidt;Sudipto Banerjee;Alan E. Gelfand;C. F. Sirmans.
Frailty modeling for spatially correlated survival data, with application to infant mortality in Minnesota.
Sudipto Banerjee;Melanie M. Wall;Bradley P. Carlin.
Improving the performance of predictive process modeling for large datasets
Andrew O. Finley;Huiyan Sang;Sudipto Banerjee;Alan E. Gelfand.
Computational Statistics & Data Analysis (2009)
Generalized Hierarchical Multivariate CAR Models for Areal Data
Xiaoping Jin;Bradley P. Carlin;Sudipto Banerjee.
Linear Algebra and Matrix Analysis for Statistics
Sudipto Banerjee;Anindya Roy.
Spatial process modelling for univariate and multivariate dynamic spatial data
Alan E. Gelfand;Sudipto Banerjee;Dani Gamerman.
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