手法を比較

	動的ベイズモデル平均 (Dynamic Bayesian Model Averaging)	逐次モンテカルロ法
分野	ベイズ	ベイズ
系統	Bayesian methods	Bayesian methods
提唱年	2010	1993 (particle filter); 2006 (SMC samplers)
提唱者	Raftery, Karny & Ettler	Gordon, Salmond & Smith (particle filter); Del Moral, Doucet & Jasra (SMC samplers)
種類	dynamic ensemble / model combination	Sequential Bayesian computation
原典	Raftery, A. E., Karny, M., & Ettler, P. (2010). Online prediction under model uncertainty via dynamic model averaging: Application to a cold rolling mill. Technometrics, 52(1), 52-66.	Gordon, N. J., Salmond, D. J., & Smith, A. F. M. (1993). Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proceedings F - Radar and Signal Processing, 140(2), 107–113.
別名	DMA, dynamic model averaging, time-varying BMA, online Bayesian model averaging	SMC, particle filter, sequential importance resampling, SMC sampler
関連	6	6
概要	Dynamic Bayesian Model Averaging (DMA) extends standard Bayesian model averaging to settings where the best predictive model may change over time. It maintains a probability distribution over a set of competing models and updates that distribution sequentially as new observations arrive, allowing model weights to evolve rather than remaining fixed across the entire sample.	Sequential Monte Carlo (SMC) is a family of simulation-based algorithms that approximate evolving probability distributions by propagating and reweighting a cloud of weighted random draws called particles. It handles nonlinear, non-Gaussian models and streams of data naturally, making it the method of choice for real-time state estimation and posterior approximation over complex distributions.