Salīdzināt metodes

	Dinamiskā Bajesas modeļu vidējo vērtību aprēķināšana	Sekvenciālā Monte Karlo metode
Nozare	Bajesa metodes	Bajesa metodes
Saime	Bayesian methods	Bayesian methods
Izcelsmes gads	2010	1993 (particle filter); 2006 (SMC samplers)
Autors	Raftery, Karny & Ettler	Gordon, Salmond & Smith (particle filter); Del Moral, Doucet & Jasra (SMC samplers)
Tips	dynamic ensemble / model combination	Sequential Bayesian computation
Pirmavots	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.
Citi nosaukumi	DMA, dynamic model averaging, time-varying BMA, online Bayesian model averaging	SMC, particle filter, sequential importance resampling, SMC sampler
Saistītās	6	6
Kopsavilkums	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.