Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Байєсівський відпал× | Метод Монте-Карло на основі Марковських ланцюгів (MCMC)× | |
|---|---|---|
| Галузь | Імітаційне моделювання | Імітаційне моделювання |
| Родина | Process / pipeline | Process / pipeline |
| Рік появи≠ | 1984 | 1953 (Metropolis-Hastings); 1984 (Gibbs) |
| Автор методу≠ | Geman, S. & Geman, D. (Bayesian framing); Kirkpatrick, S. et al. (SA foundation) | Metropolis et al. (1953); Gibbs sampler formalised by Geman & Geman (1984) |
| Тип≠ | Probabilistic metaheuristic with Bayesian inference | Simulation-based Bayesian inference / numerical integration |
| Основоположне джерело≠ | Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220(4598), 671–680. DOI ↗ | Gelman, A., Carlin, J.B., Stern, H.S., Dunson, D.B., Vehtari, A. & Rubin, D.B. (2013). Bayesian Data Analysis (3rd ed.). Chapman & Hall/CRC. DOI ↗ |
| Інші назви | BSA, Bayesian SA, Bayesian Stochastic Annealing, Bayesian Thermodynamic Optimization | MCMC, Metropolis-Hastings, Gibbs sampling, Markov Zinciri Monte Carlo (MCMC — Metropolis-Hastings, Gibbs) |
| Пов'язані | 5 | 5 |
| Підсумок≠ | Bayesian Simulated Annealing (BSA) integrates Bayesian prior knowledge about the objective landscape into the simulated annealing search process. By encoding beliefs about promising regions as prior distributions and updating them as the search progresses, BSA focuses computational effort on high-probability areas of the solution space, accelerating convergence and improving solution quality compared to uninformed SA. | Markov Chain Monte Carlo (MCMC) is a family of simulation algorithms that constructs a Markov chain whose stationary distribution is the target posterior, enabling Bayesian inference and high-dimensional integral computation that would otherwise be analytically intractable. Pioneered by Metropolis and colleagues in 1953 and extended by Hastings in 1970, MCMC underpins modern Bayesian statistics. The two most widely used variants are Metropolis-Hastings, which proposes moves from a general proposal distribution, and Gibbs sampling, which draws each parameter in turn from its full conditional distribution. |
| ScholarGateНабір даних ↗ |
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