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  "Title": "Spatiotemporal Propagation for Multivariate Bayesian Dynamic\nLearning",
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  "Description": "Implementation of the Forward Filtering Backward Sampling\n(FFBS) algorithm with Dynamic Bayesian Predictive Stacking\n(DYNBPS) integration for multivariate spatiotemporal models, as\nintroduced in \"Adaptive Markovian Spatiotemporal Transfer\nLearning in Multivariate Bayesian Modeling\" (Presicce and\nBanerjee, 2026+) <doi:10.48550/arXiv.2602.08544>. This\nmethodology enables efficient Bayesian multivariate\nspatiotemporal modeling, utilizing dynamic predictive stacking\nto improve inference across multivariate time series of spatial\ndatasets. The core functions leverage 'C++' for\nhigh-performance computation, making the framework well-suited\nfor large-scale spatiotemporal data analysis in parallel\ncomputing environments.",
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