Comparar métodos
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| Algoritmo Dinámico de Metropolis-Hastings× | Inferencia Bayesiana Dinámica× | |
|---|---|---|
| Campo | Bayesiano | Bayesiano |
| Familia | Bayesian methods | Bayesian methods |
| Año de origen≠ | 1970 (algorithm); 1992 (dynamic application) | 1989–1997 |
| Autor original≠ | W. K. Hastings (algorithm); applied to dynamic models by Carlin, Polson & Stoffer | West & Harrison (dynamic linear models); Dean & Kanazawa (dynamic Bayesian networks) |
| Tipo≠ | Bayesian MCMC sampler for dynamic models | Bayesian sequential / online inference framework |
| Fuente seminal≠ | Hastings, W. K. (1970). Monte Carlo sampling methods using Markov chains and their applications. Biometrika, 57(1), 97–109. DOI ↗ | West, M. & Harrison, J. (1997). Bayesian Forecasting and Dynamic Models (2nd ed.). Springer. ISBN: 978-0387947259 |
| Alias | 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 |
| Relacionados≠ | 5 | 6 |
| Resumen≠ | 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. |
| ScholarGateConjunto de datos ↗ |
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