Modeling product demand with discrete choice models requires specifying how all competing products rate on various attributes, which becomes difficult when the choice set is large. As a result, this paper approximates the ratings for the various product offerings with a parameterized...

We develop a Markov Chain Monte Carlo (MCMC) algorithm for estimating nested logit models in a Bayesian framework. Appropriate "heating target" and reparameterization techniques are adopted for fast mixing. For illustrative purposes, we have implemented the algorithm on two real-life examples...

The problem of revealed stochastic preference is whether probability distributions of observed choices in a population for various choice situations are consistent with a hypothesis of maximization of preference preorders by members of the population. This is a population analog of the classical...