Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Monte Carlo Sequencial Dinâmico× | Amostragem de Gibbs× | |
|---|---|---|
| Área | Bayesiano | Bayesiano |
| Família | Bayesian methods | Bayesian methods |
| Ano de origem≠ | 2006 | 1984 |
| Autor original≠ | Del Moral, Doucet, Jasra | Stuart Geman & Donald Geman |
| Tipo≠ | Sequential Monte Carlo sampler for dynamic settings | MCMC sampling algorithm |
| Fonte seminal≠ | Del Moral, P., Doucet, A. & Jasra, A. (2006). Sequential Monte Carlo samplers. Journal of the Royal Statistical Society: Series B, 68(3), 411–436. DOI ↗ | Geman, S. & Geman, D. (1984). Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6(6), 721-741. DOI ↗ |
| Outros nomes | Dynamic SMC, SMC for dynamic models, sequential particle filter, dynamic particle sampler | Gibbs sampler, coordinate-wise MCMC, systematic scan Gibbs, blocked Gibbs sampling |
| Relacionados≠ | 6 | 5 |
| Resumo≠ | Dynamic Sequential Monte Carlo (Dynamic SMC) is a Bayesian computational method that maintains and updates a population of weighted samples — particles — as new observations arrive over time. It propagates particles through a dynamic system model, reweights them by how well they match the observed data, and periodically resamples to concentrate effort on high-probability regions, yielding online posterior inference for state-space and time-evolving models. | Gibbs sampling is a Markov chain Monte Carlo algorithm that approximates a high-dimensional posterior distribution by repeatedly drawing each parameter from its full conditional distribution given all other parameters and the data. Because each draw is exact from a conditional — not a proposal that may be rejected — the sampler is efficient when those conditionals are available in closed form. |
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