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| 시계열 베이즈 추론× | 동적 베이즈 네트워크× | |
|---|---|---|
| 분야 | 베이지안 | 베이지안 |
| 계열 | Bayesian methods | Bayesian methods |
| 기원 연도 | 1989 | 1989 |
| 창시자≠ | Mike West and Jeff Harrison | Thomas Dean & Keiji Kanazawa |
| 유형≠ | Bayesian probabilistic model | probabilistic graphical model for sequences |
| 원전≠ | West, M. & Harrison, J. (1997). Bayesian Forecasting and Dynamic Models (2nd ed.). Springer. ISBN: 978-0387947259 | Dean, T. & Kanazawa, K. (1989). A model for reasoning about persistence and causation. Computational Intelligence, 5(3), 142–150. DOI ↗ |
| 별칭 | Bayesian time series analysis, Bayesian state-space modeling, probabilistic time series inference, BSTS | DBN, temporal Bayesian network, dynamic probabilistic graphical model, two-slice temporal Bayesian network |
| 관련≠ | 6 | 5 |
| 요약≠ | 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. | 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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