Editorial. Modern Bayesian Methods with Applications in Data Science
Volume 1, Issue 2 (2023), pp. 123–125
Pub. online: 15 September 2023
Type: Editorial
Open Access
Open Access
Published
15 September 2023
15 September 2023
References
Berger, J. (2022). Four Types of Frequentism and Their Interplay with Bayesianism. The New England Journal of Statistics in Data Science 1–12. https://doi.org/10.51387/22-NEJSDS4.
Berger, J. (2022). Rejoinder of “Four Types of Frequentism and Their Interplay with Bayesianism”. The New England Journal of Statistics in Data Science 1–2. https://doi.org/10.51387/22-NEJSDS4REJ.
Dey, D., Datta, A. and Banerjee, S. (2023). Modeling Multivariate Spatial Dependencies Using Graphical Models. The New England Journal of Statistics in Data Science 1–13. https://doi.org/10.51387/23-NEJSDS47.
Gu, M., Liu, X., Fang, X. and Tang, S. (2022). Scalable Marginalization of Correlated Latent Variables with Applications to Learning Particle Interaction Kernels. The New England Journal of Statistics in Data Science 1–15. https://doi.org/10.51387/22-NEJSDS13.
Halder, A., Mohammed, S. and Dey, D. K. (2023). Bayesian Variable Selection in Double Generalized Linear Tweedie Spatial Process Models. The New England Journal of Statistics in Data Science 1–13. https://doi.org/10.51387/23-NEJSDS37.
Maity, A. K. and Basu, S. (2023). Highest Posterior Model Computation and Variable Selection via Simulated Annealing. The New England Journal of Statistics in Data Science 1–8. https://doi.org/10.51387/23-NEJSDS40.
Pericchi, L. (2023). Invited Discussion of J.O. Berger: Four Types of Frequentism and Their Interplay with Bayesianism. The New England Journal of Statistics in Data Science 1–3. https://doi.org/10.51387/23-NEJSDS4B.
Porwal, A. and Raftery, A. E. (2022). Effect of Model Space Priors on Statistical Inference with Model Uncertainty. The New England Journal of Statistics in Data Science 1–10. https://doi.org/10.51387/22-NEJSDS14.
Prothero, J., Hannig, J. and Marron, J. S. (2023). New Perspectives on Centering. The New England Journal of Statistics in Data Science 1–21. https://doi.org/10.51387/23-NEJSDS31.
Rousseau, J. (2023). Discussion of: Four Types of Frequentism and Their Interplay with Bayesianism, by J. Berger. The New England Journal of Statistics in Data Science 1–2. https://doi.org/10.51387/23-NEJSDS4C.
Shen, N., González-Arévalo, B. and Pericchi, L. R. (2023). Comparison Between Bayesian and Frequentist Tail Probability Estimates. The New England Journal of Statistics in Data Science 1–8. https://doi.org/10.51387/23-NEJSDS39.
Shen, Y., Park, Y., Chakraborty, S. and Zhang, C. (2023). Bayesian Simultaneous Partial Envelope Model with Application to an Imaging Genetics Analysis. The New England Journal of Statistics in Data Science 1–33. https://doi.org/10.51387/23-NEJSDS23.
Thornton, S., Li, W. and Xie, M. (2023). Approximate Confidence Distribution Computing. The New England Journal of Statistics in Data Science 1–13. https://doi.org/10.51387/23-NEJSDS38.
van der Vaart, A. (2022). Frequentism. The New England Journal of Statistics in Data Science 1–4. https://doi.org/10.51387/22-NEJSDS4A.
Vimalajeewa, D., DasGupta, A., Ruggeri, F. and Vidakovic, B. (2023). Gamma-Minimax Wavelet Shrinkage for Signals with Low SNR. The New England Journal of Statistics in Data Science 1–13. https://doi.org/10.51387/23-NEJSDS43.
