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1. حسینی، ف. و کریمی، ا. )۱۴۰۲(، میدان تصادفی چوله نرمال بسته منعطف برای تحلیل دادههای فضایی چوله. مجله علوم آماری، ۱۷)۲(، ۳۷۱-۳۸۸. 2. حسینی، ف. و کریمی، ا. )۱۴۰۳(، تحلیل بیزی متغیرهای پنهان در مدلهای آمیخته خطی تعمیمیافته فضایی با میدان تصادفی مانای چوله گاوسی، مجله علوم آماری، ۱۸)۱(، ۵۷-۷۲. 3. 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] 4. Arellano¬-Valle, R. B., Genton, M. G. and Ferreira, M. A. R. (2021), Spatio¬temporal Skew¬t Process Models for the Analysis of Environmental Data, Spatial Statistics, 45, 100521. 5. Azzalini, A. and Capitanio, A. (2005), A Class of Multivariate Skew Distributions with Applications to Multivariate Skew¬t Distributions, Scandinavian Journal of Statistics, 32(2), 159-188. [ DOI:10.1111/j.1467-9469.2005.00426.x] 6. Bakshi, A. (2023), Spatio¬-Temporal Modeling, Dynamic Time Series Models using R¬INLA: An Applied Perspective, Chapman and Hall/CRC. 7. Betancourt, M. (2017), A Conceptual Introduction to Hamiltonian Monte Carlo, arXiv preprint, arXiv:1701.02434. 8. Castro-¬Campos, B., Gómez¬Rubio, V., and Mínguez, R. (2023), Bayesian Modeling and Clustering for Spatio-¬Temporal Areal Data: An Application to Italian Unemploy¬ Ment, Spatial Statistics, 56, 100754. 9. Cressie, N. and Wikle, C. K. (2015), Statistics for Spatio¬Temporal Data, John Wiley & Sons. 10. Genton, M. G. (2004), Skew¬-Elliptical Distributions and Their Applications: a Review, Journal of Multivariate Analysis, 89(1), 49-73. [ DOI:10.1201/9780203492000] 11. Fin, F., Idris, M. Y., Wang, Y., Banerjee, S. and Rue, H. (2024), Scalable Spatio-Tem¬poral Prediction with Bayesian Neural Fields, Nature Communications, 15, 1234. [ DOI:10.1038/s41467-024-51477-5] [ PMID] [ ] 12. Hoffman, M. D. and Gelman, A. (2014), No-¬U-¬Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo, Journal of Machine Learning Re¬search, 15(1), 1593-1623. 13. Hirt, M., Titsias, M. and Dellaportas, P. (2021), Entropy-¬Based Adaptive Hamiltonian Monte Carlo, Neural Information Processing Systems (NeurIPS), 34, 28482-28495. 14. Hosseini, F. and Karimi, O. (2024), 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] 15. Hosseini, F. and Karimi, O. (2024), Bayesian Analysis of Latent Variables in Spatial GLM Models with Stationary Skew Gaussian Random Field, Journal of Statistical Sciences, 18(1), 57-72. 16. Neal, R. M. (2011), MCMC using Hamiltonian dynamics, In: Handbook of Markov Chain Monte Carlo, Chapman and Hall/CRC. [ DOI:10.1201/b10905-6] [ PMID] 17. Nychka, D., Bandyopadhyay, S., Hammerling, D., Lindgren, F. and Sain, S. (2015), A Multiresolution Gaussian Process Model for the Analysis of Large Spatial Data Sets, Journal of Computational and Graphical Statistics, 24(2), 579-599. [ DOI:10.1080/10618600.2014.914946] 18. 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] [ ] 19. Gómez¬-Rubio, V. (2020), Bayesian Inference with INLA (1st ed), Chapman and Hall/CRC. [ DOI:10.1201/9781315175584-1] 20. Sahu, S. (2022). Bayesian Modeling of Spatio¬Temporal Data with R, Chapman and Hall/CRC. [ DOI:10.32614/CRAN.package.bmstdr] 21. Zhang, Y., Banerjee, S. and Finley, A. O. (2018), Nonstationary and non-¬Gaussian Modeling of Spatial Data Using Flexible Skew¬-t Processes, Spatial Statistics, 28, .250-232.
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