Bayesian methodsBayesian / computational
Dynamic Bayesian Network
A Dynamic Bayesian Network (DBN) extends a standard Bayesian network over time by representing how a set of random variables evolve across discrete time steps. It captures both the conditional independence structure among variables at each instant and the probabilistic dependencies between consecutive time slices, enabling principled reasoning about temporal processes under uncertainty.
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Sources
- Dean, T. & Kanazawa, K. (1989). A model for reasoning about persistence and causation. Computational Intelligence, 5(3), 142–150. DOI: 10.1111/j.1467-8640.1989.tb00324.x ↗
- Murphy, K. P. (2002). Dynamic Bayesian Networks: Representation, Inference and Learning. PhD thesis, University of California, Berkeley. link ↗
Related methods
Referenced by
Dynamic Bayesian InferenceDynamic Bayesian Model AveragingDynamic Variational InferenceHierarchical Bayesian NetworkKalman FilterMultilevel Bayesian NetworkTime series Bayesian hierarchical modelTime series Bayesian inferenceTime Series Kalman FilterTime series particle filterTime series sequential Monte Carlo