Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| MCMC para Séries Temporais× | Monte Carlo Sequencial× | |
|---|---|---|
| Área | Bayesiano | Bayesiano |
| Família | Bayesian methods | Bayesian methods |
| Ano de origem≠ | 1994–1997 | 1993 (particle filter); 2006 (SMC samplers) |
| Autor original≠ | Carter & Kohn; West & Harrison | Gordon, Salmond & Smith (particle filter); Del Moral, Doucet & Jasra (SMC samplers) |
| Tipo≠ | Bayesian posterior sampling for time-ordered data | Sequential Bayesian computation |
| Fonte seminal≠ | Carter, C. K. & Kohn, R. (1994). On Gibbs sampling for state space models. Biometrika, 81(3), 541–553. DOI ↗ | 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. DOI ↗ |
| Outros nomes | MCMC time series, Bayesian time series MCMC, time series posterior sampling, sequential Bayesian MCMC | SMC, particle filter, sequential importance resampling, SMC sampler |
| Relacionados | 6 | 6 |
| Resumo≠ | Time series MCMC applies Markov chain Monte Carlo methods to Bayesian inference over time-ordered data. Rather than optimising a single parameter estimate, it draws samples from the full joint posterior of parameters and latent states, yielding probability distributions that honestly reflect uncertainty about dynamics, trends, and seasonal patterns across every time point. | 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. |
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