Peruzzi, Michele, Banerjee, Sudipto, and Finley, Andrew O. Highly Scalable Bayesian Geostatistical Modeling via Meshed Gaussian Processes on Partitioned Domains. Retrieved from https://par.nsf.gov/biblio/10338186. Journal of the American Statistical Association 117.538 Web. doi:10.1080/01621459.2020.1833889.

Peruzzi, Michele, Banerjee, Sudipto, & Finley, Andrew O. Highly Scalable Bayesian Geostatistical Modeling via Meshed Gaussian Processes on Partitioned Domains. Journal of the American Statistical Association, 117 (538). Retrieved from https://par.nsf.gov/biblio/10338186. https://doi.org/10.1080/01621459.2020.1833889

Peruzzi, Michele, Banerjee, Sudipto, and Finley, Andrew O. "Highly Scalable Bayesian Geostatistical Modeling via Meshed Gaussian Processes on Partitioned Domains". Journal of the American Statistical Association 117 (538). Country unknown/Code not available. https://doi.org/10.1080/01621459.2020.1833889. https://par.nsf.gov/biblio/10338186.

@article{osti_10338186,
place = {Country unknown/Code not available}, title = {Highly Scalable Bayesian Geostatistical Modeling via Meshed Gaussian Processes on Partitioned Domains}, url = {https://par.nsf.gov/biblio/10338186}, DOI = {10.1080/01621459.2020.1833889}, abstractNote = {}, journal = {Journal of the American Statistical Association}, volume = {117}, number = {538}, author = {Peruzzi, Michele and Banerjee, Sudipto and Finley, Andrew O.}, }