Discrete Choice Methods with Simulation Kenneth E. Train is an English-language textbook that provides insight into advanced discrete choice methods with simulation. This reference work focuses on researchers and professionals in various fields such as energy, transportation, and marketing.
The book extensively covers discrete choice models such as logit, generalized extreme value (GEV), probit, and mixed logit. It also addresses recent developments in Bayesian methods, including the Metropolis-Hastings algorithm and Gibbs sampling. The second edition introduces additional chapters on endogeneity and expectation-maximization (EM) algorithms.
With practical applications of choice methods in economic and statistical contexts, this work offers a comprehensive overview of discrete choice methods relevant for decision-making and analysis across multiple disciplines.
Topics connect with decision theory, econometrics, economic statistics, and game theory.