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publications
Bayesian Inference for Spatial-Temporal Non-Gaussian Data Using Predictive Stacking
arXiv preprint, 2024
We develop Bayesian predictive stacking algorithm for analysis of non-Gaussian geospatial data.
Pan, S., Zhang, L., Bradley, J. R., & Banerjee, S. (2024). "Bayesian inference for spatial-temporal non-Gaussian data using predictive stacking." arXiv:stat.ME.
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Bayesian hierarchical modeling and inference for mechanistic systems in industrial hygiene
Annals of Work Exposures and Health, 2024
A Bayesian state-space modeling framework that synthesizes information from the mechanistic system as well as the field data.
Pan, S., Das, D., Ramachandran, G., & Banerjee, S. (2024). "Bayesian hierarchical modeling and inference for mechanistic systems in industrial hygiene." Annals of Work Exposures and Health, 68(8), 834–845. Preprint, 2024 This manuscript introduces the R package spStack that delivers Bayesian inferece for geospatial data using predictive stacking. Pan, S. & Banerjee, S. (2024). "spStack: Practical Bayesian Geostatistics Using Predictive Stacking in R." Fast Bayesian inference for Gaussian and non-Gaussian geospatial models without using Markov chain Monte Carlo algorithms. This R package is written in C++ with calls to FORTRAN routines for optimized linear algebra operations. Published: Session title: Recent advances in uncertainty quantification for complex systems. Undergraduate course, University 1, Department, 2014 This is a description of a teaching experience. You can use markdown like any other post. Workshop, University 1, Department, 2015 This is a description of a teaching experience. You can use markdown like any other post.
Download Citation | DOI</p> </article> </div> spStack: Practical Bayesian Geostatistics Using Predictive Stacking in R
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spStack: Bayesian Geostatistics Using Predictive Stacking
Now available on CRAN. Also check out its new website! 
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Bayesian Hierarchical Modeling and Inference for Mechanistic Systems: An Application in Industrial Hygiene
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