So sánh phương pháp

	Mô hình Logit hỗn hợp	Mô phỏng Monte Carlo
Lĩnh vực	Kinh tế lượng	Ra quyết định
Họ	Regression model	MCDM
Năm ra đời	2000	1949
Người khởi xướng	Daniel McFadden & Kenneth Train	Metropolis, N., Ulam, S.
Loại	Random-parameters discrete choice model	Robustness wrapper — Monte Carlo uncertainty propagation
Công trình gốc	Train, K. E. (2009). Discrete Choice Methods with Simulation (2nd ed.). Cambridge University Press. ISBN: 978-0-521-74738-7	Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association
Tên gọi khác	Random Parameters Logit, Mixed Multinomial Logit, Error Components Logit, Karma Logit Modeli	—
Liên quan	3	0
Tóm tắt	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.	MONTE-CARLO-SIMULATION (Monte Carlo Simulation — Stochastic uncertainty propagation through MCDM model) is a ranking multi-criteria decision-making (MCDM) method introduced by Metropolis, N., Ulam, S. in 1949. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.