Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Markov Chain Monte Carlo Imara× | Monte Carlo Sekwenshiali× | |
|---|---|---|
| Nyanja | Mbinu za Bayes | Mbinu za Bayes |
| Familia | Bayesian methods | Bayesian methods |
| Mwaka wa asili≠ | 2000s–2010s | 1993 (particle filter); 2006 (SMC samplers) |
| Mwanzilishi≠ | Roberts, Rosenthal and colleagues; extended by Atchade, Barp, Girolami and others | Gordon, Salmond & Smith (particle filter); Del Moral, Doucet & Jasra (SMC samplers) |
| Aina≠ | Bayesian computational sampling | Sequential Bayesian computation |
| Chanzo asilia≠ | Roberts, G. O. & Rosenthal, J. S. (2004). General state space Markov chains and MCMC algorithms. Probability Surveys, 1, 20–71. 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 ↗ |
| Majina mbadala | robust MCMC, outlier-robust MCMC, robust posterior sampling, misspecification-robust MCMC | SMC, particle filter, sequential importance resampling, SMC sampler |
| Zinazohusiana≠ | 5 | 6 |
| Muhtasari≠ | Robust MCMC combines Markov chain Monte Carlo sampling with robustness techniques to produce reliable posterior inference when data contain outliers, when the assumed model is misspecified, or when the target distribution has heavy tails that cause standard samplers to mix poorly or yield distorted estimates. | 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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