পদ্ধতির তুলনা করুন
নির্বাচিত পদ্ধতিগুলো পাশাপাশি পর্যালোচনা করুন; যে সারিগুলোয় পার্থক্য আছে সেগুলো চিহ্নিত করা হয়।
| টাইম সিরিজ ভ্যারিয়েশনাল ইনফারেন্স× | সিকোয়েনশিয়াল মন্টে কার্লো× | |
|---|---|---|
| ক্ষেত্র | বেইসীয় | বেইসীয় |
| পরিবার | Bayesian methods | Bayesian methods |
| উদ্ভবের বছর≠ | 1999–2017 | 1993 (particle filter); 2006 (SMC samplers) |
| প্রবর্তক≠ | Jordan, Ghahramani, Jaakkola, Saul; extended by Blei and colleagues | Gordon, Salmond & Smith (particle filter); Del Moral, Doucet & Jasra (SMC samplers) |
| ধরন≠ | Approximate Bayesian inference | Sequential Bayesian computation |
| মৌলিক উৎস≠ | Blei, D. M., Kucukelbir, A. & McAuliffe, J. D. (2017). Variational inference: A review for statisticians. Journal of the American Statistical Association, 112(518), 859-877. 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 ↗ |
| অপর নাম | time-series VI, variational Bayes for time series, TSVI, sequential variational inference | SMC, particle filter, sequential importance resampling, SMC sampler |
| সম্পর্কিত | 6 | 6 |
| সারসংক্ষেপ≠ | Time series variational inference applies variational Bayes to sequential data, approximating the intractable posterior over latent states and parameters with a tractable family of distributions. By maximising the evidence lower bound (ELBO), it delivers fast, scalable Bayesian inference for state-space models, dynamic latent variable models, and other time-ordered probabilistic systems. | 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. |
| ScholarGateডেটাসেট ↗ |
|
|