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Time series Bayesian inference

Time series Bayesian inference applies Bayes' theorem sequentially to time-ordered observations, maintaining a full probability distribution over hidden states and model parameters at every time step. This framework unifies state-space models, dynamic linear models, and particle filters, producing calibrated uncertainty for both filtering (real-time) and retrospective smoothing tasks.

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Bayesian Inference for Time Series Models

Taksonoomiline meetodikirje · bayesian / bayesian

West, M. & Harrison, J. (1997). Bayesian Forecasting and Dynamic Models (2nd ed.). Springer. · ISBN 978-0387947259
Prado, R. & West, M. (2010). Time Series: Modeling, Computation, and Inference. CRC Press. · ISBN 978-1420093360

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Same method familyBayesian Regressionmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketDynamic Bayesian Networkmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketHierarchical Bayesian Inferencemachine-suggested · Relational suggestion, not evidence.Taxonomic bucketKalman Filtermachine-suggested · Relational suggestion, not evidence.Same method familyParticle Filtermachine-suggested · Relational suggestion, not evidence.Taxonomic bucketSequential Monte Carlomachine-suggested · Relational suggestion, not evidence.