পদ্ধতির তুলনা করুন

	বেয়েশীয় মন্টি কার্লো সিমুলেশন	বেয়েশীয় সংবেদনশীলতা বিশ্লেষণ
ক্ষেত্র	অনুকরণ	অনুকরণ
পরিবার	Process / pipeline	Process / pipeline
উদ্ভবের বছর	1987–1990s	1984–1994
প্রবর্তক	O'Hagan, A. and colleagues	Berger, J. O. (Bayesian robustness); Saltelli et al. (global SA integration)
ধরন	Simulation / uncertainty quantification	Uncertainty propagation and sensitivity quantification
মৌলিক উৎস	O'Hagan, A., Buck, C. E., Daneshkhah, A., Eiser, J. R., Garthwaite, P. H., Jenkinson, D. J., Oakley, J. E., & Rakow, T. (2006). Uncertain Judgements: Eliciting Experts' Probabilities. Wiley. ISBN: 9780470029992	Berger, J. O. (1994). An overview of robust Bayesian analysis. Test, 3(1), 5–124.
অপর নাম	Bayesian MC, BMC simulation, Bayesian stochastic simulation, Bayesian uncertainty propagation	BSA, Bayesian SA, Bayesian robustness analysis, prior sensitivity analysis
সম্পর্কিত	4	5
সারসংক্ষেপ	Bayesian Monte Carlo Simulation integrates Bayesian statistical inference with Monte Carlo sampling to propagate uncertainty through complex models. Instead of drawing samples from arbitrary distributions, it conditions sampling on observed data and expert prior knowledge via Bayes' theorem, yielding posterior-based uncertainty estimates that are both statistically coherent and interpretable in probabilistic terms.	Bayesian Sensitivity Analysis (BSA) combines Bayesian inference with sensitivity analysis to systematically quantify how uncertain model inputs — expressed as prior probability distributions — propagate through a model and influence outputs. It identifies which parameters most drive output variability, supporting robust conclusions under genuine uncertainty.