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| 시계열 근사 베이즈 추론× | 시계열 베이즈 추론× | |
|---|---|---|
| 분야 | 베이지안 | 베이지안 |
| 계열 | Bayesian methods | Bayesian methods |
| 기원 연도≠ | 2009 | 1989 |
| 창시자≠ | Beaumont, Zhang & Balding (2002) for ABC; Toni et al. (2009) for dynamical/time-series extension | Mike West and Jeff Harrison |
| 유형≠ | likelihood-free Bayesian inference | Bayesian probabilistic model |
| 원전≠ | Toni, T., Welch, D., Strelkowa, N., Ipsen, A. & Stumpf, M. P. H. (2009). Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems. Journal of the Royal Society Interface, 6(31), 187–202. DOI ↗ | West, M. & Harrison, J. (1997). Bayesian Forecasting and Dynamic Models (2nd ed.). Springer. ISBN: 978-0387947259 |
| 별칭 | TS-ABC, time series ABC, likelihood-free inference for time series, ABC for dynamical systems | Bayesian time series analysis, Bayesian state-space modeling, probabilistic time series inference, BSTS |
| 관련 | 6 | 6 |
| 요약≠ | Time series ABC is a likelihood-free Bayesian inference method that estimates the posterior distribution of model parameters for dynamical or time-indexed systems by comparing summary statistics of simulated trajectories to those of the observed series, bypassing the need to evaluate an analytic likelihood. It is particularly valuable for complex mechanistic or stochastic models whose likelihoods are intractable. | 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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