I wish to congratulate the authors on their comparison of the newly proposed generalized multinomial logit (GMNL) of Fiebig et al. [1], henceforth ‘FKLW,’ with the widely-used, latent class or finite mixture model of Kamakura and Russell [2]. Both models use the same logistic regression likelihood for product choice but differ in their distributions for individual parameter heterogeneity. I am ...

The multinomial probit (MNP) model is a primary application for combining simulation with estimation. Indeed, McFadden (1989) featured the MNP model in his seminal paper. As random utility model, the MNP model offers a highly desirable flexibility in substitution among alternatives that its chief rival, the multinomial logit model, fails to possess. The unrestricted character of the variance ma...

In attempts to conserve the species diversity of trees in tropical forests, monitoring of diversity in inventories is essential. For effective monitoring it is crucial to be able to make meaningful comparisons between the diversity between different regions, or comparisons of the diversity of a region at different times. Many species diversity measures have been defined, including the well-know...

We introduce topographic versions of two latent class models (LCM) for collaborative filtering. Latent classes are topologically organized on a square grid. Topographic organization of latent classes makes orientation in rating/preference patterns captured by the latent classes easier and more systematic. The variation in film rating patterns is modelled by multinomial and binomial distribution...

The model A hidden Markov model is characterized by a set of M states, by an initial probability distribution for the first state, by a transition probability matrix linking successive states, and by a state-dependent probability distribution on the outputs. We represent the state at time t as a multinomial random variable qt, with components q t, for i = 1, . . . ,M . Thus q t is equal to one ...

In many machine learning applications that deal with sequences, there is a need for learning algorithms that can effectively utilize the hierarchical grouping of words. We introduce Word Taxonomy guided Naive Bayes Learner for the Multinomial Event Model (WTNBL-MN) that exploits word taxonomy to generate compact classifiers, and Word Taxonomy Learner (WTL) for automated construction of word tax...

Two models for jointly analysing the spatial variation of incidences of three (or more) diseases, with common and uncommon risk factors, are compared via a simulation experiment. In both models, the linear predictor can be decomposed into shared and disease-specific spatial variability components (named shared clustering and specific clustering respectively). The two models are the shared model...

A rich class of parametric models is proposed for discrete choice data based on the scale mixture of multivariate normal distributions. The multinomial probit model is a special case in the class. The new models can be implemented in a Bayesian framework without much difficulty because of their special connections to the multinomial probit model. A Gibbs sampler with data augmentation is used t...