পদ্ধতির তুলনা করুন
নির্বাচিত পদ্ধতিগুলো পাশাপাশি পর্যালোচনা করুন; যে সারিগুলোয় পার্থক্য আছে সেগুলো চিহ্নিত করা হয়।
| মেট্রোপলিস-হেস্টিংস অ্যালগরিদম× | স্লাইস স্যাম্পলিং× | |
|---|---|---|
| ক্ষেত্র | বেইসীয় | বেইসীয় |
| পরিবার | Bayesian methods | Bayesian methods |
| উদ্ভবের বছর≠ | 1953 | 2003 |
| প্রবর্তক≠ | Metropolis et al. (1953); generalised by Hastings (1970) | Radford M. Neal |
| ধরন≠ | Markov chain Monte Carlo sampler | MCMC sampling algorithm |
| 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 ↗ | Neal, R. M. (2003). Slice sampling (with discussion). Annals of Statistics, 31(3), 705–767. DOI ↗ |
| অপর নাম≠ | MH algorithm, M-H algorithm, Metropolis algorithm, Metropolis-Hastings sampler | slice sampler, Neal slice sampler, uniform slice sampling, auxiliary variable slice sampler |
| সম্পর্কিত≠ | 5 | 4 |
| 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. | Slice sampling is a Markov chain Monte Carlo (MCMC) algorithm introduced by Radford M. Neal in his 2003 Annals of Statistics paper. It generates samples from a target distribution by drawing uniformly from the region under the density curve — called the 'slice' — without requiring the user to specify a step-size or proposal distribution, making it self-tuning and broadly applicable for Bayesian posterior inference. |
| ScholarGateডেটাসেট ↗ |
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