পদ্ধতির তুলনা করুন

	বিচ্ছিন্ন পছন্দ অনুকরণ (Discrete Choice Simulation)	কনজয়েন্ট অ্যানালাইসিস	মিশ্র লজিট মডেল
ক্ষেত্র	অনুকরণ	পরীক্ষামূলক নকশা	অর্থমিতি
পরিবার	Process / pipeline	Hypothesis test	Regression model
উদ্ভবের বছর	1974 (McFadden's Nobel-cited logit); simulation extensions throughout 1990s–2000s	1978	2000
প্রবর্তক	Daniel McFadden (random utility theory); Kenneth Train (simulation methods)	Paul E. Green & V. Srinivasan	Daniel McFadden & Kenneth Train
ধরন	Discrete choice modelling with Monte Carlo simulation	Decomposition-based utility estimation	Random-parameters discrete choice model
মৌলিক উৎস	Train, K.E. (2009). Discrete Choice Methods with Simulation (2nd ed.). Cambridge University Press.	Green, P.E. & Srinivasan, V. (1978). Conjoint analysis in consumer research: Issues and outlook. Journal of Consumer Research, 5(2), 103–123.	Train, K. E. (2009). Discrete Choice Methods with Simulation (2nd ed.). Cambridge University Press. ISBN: 978-0-521-74738-7
অপর নাম	stated preference simulation, SP simulation, revealed preference modelling, Ayrık Seçim Simülasyonu (Stated Preference / SP Simulation)	CBC conjoint, choice-based conjoint, adaptive conjoint analysis, full-profile conjoint	Random Parameters Logit, Mixed Multinomial Logit, Error Components Logit, Karma Logit Modeli
সম্পর্কিত	5	6	3
সারসংক্ষেপ	Discrete choice simulation is a behavioural modelling method — grounded in random utility theory formalised by Daniel McFadden in the 1970s and extended to simulation-based estimation by Kenneth Train — that estimates how individuals choose among mutually exclusive alternatives and then uses those estimated preference parameters to forecast how choice shares would shift under hypothetical policy or market scenarios. It is the dominant quantitative tool in transport demand analysis, health economics, environmental valuation, and marketing research.	Conjoint analysis is a preference-measurement technique that decomposes overall product evaluations into the separate utility values — called part-worths — that respondents assign to each attribute level. Formalised by Green and Srinivasan in their seminal 1978 Journal of Consumer Research paper, the method has become the dominant tool in marketing research and product design for quantifying what buyers truly trade off when they choose between options.	The Mixed Logit model, introduced formally by McFadden and Train (2000) and elaborated in Train (2009), is a flexible discrete choice framework that allows preference parameters to vary randomly across decision-makers. By integrating standard logit probabilities over a mixing distribution of coefficients, it overcomes the restrictive independence of irrelevant alternatives (IIA) property and accommodates unobserved taste heterogeneity, panel data correlation, and complex substitution patterns across alternatives.