Conference paper
Springer Proceedings in Mathematics and Statistics, New Frontiers in Bayesian Statistics, BAYSM 2021: Selected Contributions, 2022, pp. 45-56
Reader in Statistics and Data Science
School of Mathematics
University of Edinburgh
Room 5406
James Clerk Maxwell Building, Edinburgh, EH9 3FD
APA
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Teo, M., & Wade, S. (2022). Bayesian nonparametric scalar-on-image regression via Potts-Gibbs random partition models. In New Frontiers in Bayesian Statistics, BAYSM 2021: Selected Contributions (pp. 45–56). https://doi.org/10.1007/978-3-031-16427-9_5
Chicago/Turabian
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Teo, Mica, and Sara Wade. “Bayesian Nonparametric Scalar-on-Image Regression via Potts-Gibbs Random Partition Models.” In New Frontiers in Bayesian Statistics, BAYSM 2021: Selected Contributions, 45–56. Springer Proceedings in Mathematics and Statistics, 2022.
MLA
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Teo, Mica, and Sara Wade. “Bayesian Nonparametric Scalar-on-Image Regression via Potts-Gibbs Random Partition Models.” New Frontiers in Bayesian Statistics, BAYSM 2021: Selected Contributions, 2022, pp. 45–56, doi:10.1007/978-3-031-16427-9_5.
BibTeX Click to copy
@inproceedings{mica2022a,
title = {Bayesian nonparametric scalar-on-image regression via Potts-Gibbs random partition models},
year = {2022},
pages = {45-56},
series = {Springer Proceedings in Mathematics and Statistics},
doi = {10.1007/978-3-031-16427-9_5},
author = {Teo, Mica and Wade, Sara},
booktitle = {New Frontiers in Bayesian Statistics, BAYSM 2021: Selected Contributions}
}