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| Monte Carlo Sekuensial× | Markov Chain Monte Carlo (MCMC)× | |
|---|---|---|
| Bidang | Bayesian | Bayesian |
| Keluarga | Bayesian methods | Bayesian methods |
| Tahun asal≠ | 1993 (particle filter); 2006 (SMC samplers) | — |
| Pencetus≠ | Gordon, Salmond & Smith (particle filter); Del Moral, Doucet & Jasra (SMC samplers) | — |
| Tipe≠ | Sequential Bayesian computation | Posterior sampling algorithm |
| Sumber perintis≠ | 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 ↗ | Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. ISBN: 978-1439840955 |
| Alias≠ | SMC, particle filter, sequential importance resampling, SMC sampler | markov chain monte carlo, MCMC sampling, MCMC (Markov Zinciri Monte Carlo) |
| Terkait≠ | 6 | 3 |
| Ringkasan≠ | 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. | Markov Chain Monte Carlo (MCMC) is a family of computational algorithms for sampling from complex probability distributions, most commonly the posterior distributions that arise in Bayesian inference. Rather than computing posteriors analytically — which is rarely possible for realistic models — MCMC constructs a Markov chain whose stationary distribution is the target posterior and draws dependent samples from it, enabling full probabilistic inference for virtually any model. |
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