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portfolio

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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.
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spStack: Practical Bayesian Geostatistics Using Predictive Stacking in R

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."
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software

spStack: Bayesian Geostatistics Using Predictive Stacking

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.

Now available on CRAN. Also check out its new website!

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015