Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Particle Filter (Sequential Monte Carlo)× | Mārkova ķēžu Montekarlo (MCMC)× | |
|---|---|---|
| Nozare | Bajesa metodes | Bajesa metodes |
| Saime | Bayesian methods | Bayesian methods |
| Izcelsmes gads≠ | 1993 | — |
| Autors≠ | Gordon, Salmond & Smith | — |
| Tips≠ | Sequential Monte Carlo estimator | Posterior sampling algorithm |
| Pirmavots≠ | 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 |
| Citi nosaukumi≠ | SMC, sequential Monte Carlo, bootstrap filter, condensation algorithm | markov chain monte carlo, MCMC sampling, MCMC (Markov Zinciri Monte Carlo) |
| Saistītās≠ | 4 | 3 |
| Kopsavilkums≠ | The particle filter, introduced by Gordon, Salmond, and Smith in 1993, is a sequential Monte Carlo algorithm that approximates the Bayesian filtering distribution for nonlinear and non-Gaussian state-space models. Rather than tracking a single best estimate, it maintains a cloud of N weighted random samples — particles — that collectively represent the full posterior distribution of a hidden state at each point in time as new observations arrive. | 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. |
| ScholarGateDatu kopa ↗ |
|
|