|
1. کریمی، الف. و حسینی، ف. (1402). میدان تصادفی چوله نرمال بسته منعطف برای تحلیل دادههای فضایی چوله. مجله علوم آماری، 17(2)، 371-388 2. کریمی، الف. و حسینی، ف. (1400). معرفی یک میدان تصادفی مانای چوله گاوسی. مجله علوم آماری، 15(2)، 549-566 3. کریمی، الف. و حسینی، ف. (1403). تحلیل بیز مقداری مدل رگرسیون فضایی چوله بر اساس زیررده منعطفی از توزیع چوله نرمال بسته. مجله علوم آماری، 18(2)، 127-142 4. حسینی، ف. و کریمی، ا. (1403). تحلیل بیزی متغیرهای پنهان در مدلهای آمیخته خطی تعمیمیافته فضایی با میدان تصادفی مانای چوله گاوسی. مجله علوم آماری، 18(1)، 57-72 5. Allard, D., and Naveau, P. (2007). A new spatial skew-normal random field model. Communications in Statistics-Theory and Methods, 36, 1821-1834. [ DOI:10.1080/03610920601126290] 6. Arellano-Valle, R. B., Ferreira, C. S., and Genton, M. G. (2018). Scale and shape mixtures of multivariate skew-normal distributions. Journal of Multivariate Analysis, 166, 98-110. [ DOI:10.1016/j.jmva.2018.02.007] 7. Azzalini, A., and Capitanio, A. (1999). Statistical applications of the multivariate skew normal distribution. Journal of the Royal Statistical Society: Series B, 61(3), 579-602. [ DOI:10.1111/1467-9868.00194] 8. Bevilacqua, M., Caamaño-Carrillo, C., Arellano-Valle, R. B., and Morales-Oñate, V. (2020). Nonstationary and non-Gaussian modeling of spatial data using flexible skew-t processes. Scandinavian Journal of Statistics, 48, 212-245. [ DOI:10.1111/sjos.12447] 9. Betancourt, M. (2017). A conceptual introduction to Hamiltonian Monte Carlo. arXiv preprint, arXiv:1701.02434. 10. Bingham, E., Chen, J. P., Jankowiak, M., Obermeyer, F., Pradhan, N., Karaletsos, T., Singh, R., Szerlip, P. A., Horsfall, P., & Goodman, N. D. (2019). Pyro: Deep universal probabilistic programming. Journal of Machine Learning Research, 20(28), 1-6. [ DOI:10.1145/3315508.3329974] [ PMID] 11. Blei, D. M., Kucukelbir, A., and McAuliffe, J. D. (2017). Variational inference: A review for statisticians. Journal of the American Statistical Association, 112(518), 859-877. [ DOI:10.1080/01621459.2017.1285773] [ ] 12. Blundell, C., Cornebise, J., Kavukcuoglu, K., and Wierstra, D. (2015). Weight uncertainty in neural networks. In Proceedings of the 32nd International Conference on Machine Learning (ICML) (pp. 1613-1622). 13. Cressie, N., and Wikle, C. K. (2011). Statistics for Spatio-Temporal Data. John Wiley & Sons. 14. Fortunato, M., Blundell, C., and Vinyals, O. (2017). Bayesian recurrent neural networks. arXiv preprint, arXiv:1704.02798. 15. Genton, M. G. (2004). Skew-Elliptical Distributions and Their Applications: A Journey beyond Normality. Chapman & Hall/CRC. 16. Gómez-Rubio, V. (2020). Bayesian Inference with INLA (1st ed.). Chapman & Hall/CRC. [ DOI:10.1201/9781315175584-1] 17. Hoffman, M. D., and Gelman, A. (2014). The No-U-Turn sampler: Adaptively setting path lengths in Hamiltonian Monte Carlo. Journal of Machine Learning Research, 15(1), 1351-1381. 18. Hosseini, F., and Karimi, O. (2021). Approximate pairwise likelihood inference in SGLM models with skew normal latent variables. Journal of Computational and Applied Mathematics, 398, 113692. [ DOI:10.1016/j.cam.2021.113692] 19. Hosseini, F., and Karimi, A. (2024). Bayesian analysis of latent variables in spatial generalized linear mixed models with a stationary skew-Gaussian random field. Journal of Statistical Sciences, 18(1), 57-72. 20. Jospin, L. V., Laga, H., Boussaid, F., and Buntine, W. (2022). Hands-on Bayesian neural networks-A tutorial for deep learning users. IEEE Computational Intelligence Magazine, 17(2), 29-48. [ DOI:10.1109/MCI.2022.3155327] 21. Karimi, A., and Hosseini, F. (2023). Flexible closed skew-normal random field to analysis skew spatial data. Journal of Statistical Sciences, 17(2), 371-388. [ DOI:10.61186/jss.17.2.12] 22. Karimi, A., and Hosseini, F. (2021). Introducing a stationary skew-Gaussian random field. Journal of Statistical Sciences, 15(2), 549-566. [ DOI:10.52547/jss.15.2.549] 23. Karimi, A., and Hosseini, F. (2024). Variational Bayesian analysis of a skew spatial regression model based on a flexible subclass of the closed skew-normal distribution. Journal of Statistical Sciences, 18(2), 127-142. [ DOI:10.61186/jss.18.2.3] 24. Márquez-Urbina, O. U., and González-Farías, G. (2022). A flexible special case of the CSN for spatial modeling and prediction. Spatial Statistics, 47, 100556. [ DOI:10.1016/j.spasta.2021.100556] [ PMID] [ ] 25. McDermott, P. L., and Wikle, C. K. (2019). Bayesian recurrent neural network models for forecasting and quantifying uncertainty in spatio-temporal data. Entropy, 21(2), 184. [ DOI:10.3390/e21020184] [ PMID] [ ] 26. Mozdzen, A., Cremaschi, A., Cadonna, A., Guglielmi, A., and Kastner, G. (2022). Bayesian modeling and clustering for spatio-temporal areal data: An application to Italian unemployment. Spatial Statistics, 52, 100715. [ DOI:10.1016/j.spasta.2022.100715] 27. Neal, R. M. (2011). MCMC using Hamiltonian dynamics. In Handbook of Markov Chain Monte Carlo. Chapman & Hall/CRC. [ DOI:10.1201/b10905-6] 28. Ravishanker, N., Raman, B., and Soyer, R. (2022). Dynamic Time Series Models using R-INLA: An Applied Perspective. Chapman & Hall/CRC. [ DOI:10.1201/9781003134039] 29. Rimstad, K., and Omre, H. (2014). Skew-Gaussian random fields. Spatial Statistics, 10, 43-62. [ DOI:10.1016/j.spasta.2014.08.001] 30. Saad, F., Burnim, J., Carroll, C., Patton, B., Koster, U., Saurous, R. A., & Hoffman, M. (2024). Scalable spatiotemporal prediction with Bayesian neural fields. Nature Communications, 15, 1234. [ DOI:10.1038/s41467-024-51477-5] [ PMID] [ ] 31. Sahu, S. K. (2022). Bayesian Modeling of Spatio-Temporal Data with R. Chapman & Hall/CRC. [ DOI:10.32614/CRAN.package.bmstdr] 32. Zammit-Mangion, A., Kaminski, M. D., Tran, B.-H., Filippone, M., and Cressie, N. (2024). Spatial Bayesian neural networks. Spatial Statistics, 60, 100825. [ DOI:10.1016/j.spasta.2024.100825] |
