Methoden vergleichen

	Dynamischer Metropolis-Hastings-Algorithmus	Dynamische Bayes'sche Inferenz
Fachgebiet	Bayes-Statistik	Bayes-Statistik
Familie	Bayesian methods	Bayesian methods
Entstehungsjahr	1970 (algorithm); 1992 (dynamic application)	1989–1997
Urheber	W. K. Hastings (algorithm); applied to dynamic models by Carlin, Polson & Stoffer	West & Harrison (dynamic linear models); Dean & Kanazawa (dynamic Bayesian networks)
Typ	Bayesian MCMC sampler for dynamic models	Bayesian sequential / online inference framework
Wegweisende Quelle	Hastings, W. K. (1970). Monte Carlo sampling methods using Markov chains and their applications. Biometrika, 57(1), 97–109.	West, M. & Harrison, J. (1997). Bayesian Forecasting and Dynamic Models (2nd ed.). Springer. ISBN: 978-0387947259
Aliasnamen	Dynamic MH, MH for state-space models, Metropolis-Hastings in dynamic models, time-varying parameter MH	online Bayesian inference, sequential Bayesian updating, recursive Bayesian estimation, dynamic Bayesian updating
Verwandt	5	6
Zusammenfassung	The Dynamic Metropolis-Hastings (Dynamic MH) algorithm applies the Metropolis-Hastings MCMC sampler to Bayesian state-space and time-varying parameter models. At each time step, latent states or evolving parameters are updated via proposal-and-accept moves, yielding full posterior distributions over trajectories rather than single filtered estimates.	Dynamic Bayesian inference is a framework for performing Bayesian updating sequentially as new observations arrive over time. Rather than fitting a static model to a fixed dataset, it tracks how a posterior distribution over latent states or parameters evolves step by step, combining a prior with each new likelihood to produce an updated posterior that propagates forward through time.