পদ্ধতির তুলনা করুন
নির্বাচিত পদ্ধতিগুলো পাশাপাশি পর্যালোচনা করুন; যে সারিগুলোয় পার্থক্য আছে সেগুলো চিহ্নিত করা হয়।
| মেট্রোপলিস-হেস্টিংস অ্যালগরিদম× | বেয়েশীয় রিগ্রেশন× | |
|---|---|---|
| ক্ষেত্র | বেইসীয় | বেইসীয় |
| পরিবার | Bayesian methods | Bayesian methods |
| উদ্ভবের বছর≠ | 1953 | — |
| প্রবর্তক≠ | Metropolis et al. (1953); generalised by Hastings (1970) | — |
| ধরন≠ | Markov chain Monte Carlo sampler | Bayesian linear model |
| মৌলিক উৎস≠ | Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., & Teller, E. (1953). Equation of state calculations by fast computing machines. The Journal of Chemical Physics, 21(6), 1087–1092. 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 |
| অপর নাম≠ | MH algorithm, M-H algorithm, Metropolis algorithm, Metropolis-Hastings sampler | bayesian linear regression, probabilistic regression, bayesian regresyon |
| সম্পর্কিত≠ | 5 | 2 |
| সারসংক্ষেপ≠ | The Metropolis-Hastings (MH) algorithm is a general-purpose Markov chain Monte Carlo (MCMC) method for drawing samples from any probability distribution whose density can be evaluated up to a normalising constant. Introduced by Metropolis, Rosenbluth, Rosenbluth, Teller, and Teller (1953) in computational physics and generalised by Hastings (1970) to asymmetric proposal distributions, it is the foundational algorithm from which nearly all subsequent MCMC samplers — Gibbs sampling, Hamiltonian Monte Carlo, slice sampling — are derived or can be viewed as special cases. | Bayesian regression is a probabilistic version of linear regression that treats the model parameters as uncertain quantities. Instead of returning a single best-fit estimate, it combines prior knowledge with the observed data to produce a full posterior probability distribution for each parameter, from which credible intervals and predictions are read off. |
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