Nonparametric Bayesian Discrete Latent Variable Models for ...

4 Indian Buffet Process Models
P+
P
R
R+
Figure 4.15: Random binary feature matrix samples weighted with the feature weights from
the chain for the Paris-Rome example. The gray level indicates the weight of each
feature. Rows correspond to options and columns to features. Only the active
features are shown. It is inferred that there are features that only P and P +
share, and only R and R+ share. Note that there are also some features common
to all options which do not affect the likelihood. The first four columns in these
figures are the unique features, remaining columns are from the posterior of the
infinite part. Note that the weights of the unique features for P and R are very
close to zero, which is expected since they are not supported by the data. The
unique features for P + and R+ have small weights representing the small bonus.
representations may result in the same choice probabilities. The important point is that
the model can learn the true choice probabilities by inferring that there are features
that only P and P + share, and there are features that only R and R+ share. The
small bonus that R+ and P + have in common is represented as two different features
with similar weights. Models on a larger set of alternatives might result in feature
representations that cannot be interpreted easily, as will be seen in the next example.
Celebrities
As a second example we analyze real choice data from an experiment by Rumelhart
and Greeno (1971) that is known to exhibit choice patterns that the BTL model cannot
capture. Subjects were presented with pairs of celebrities and were asked with whom
they would prefer to spend an hour of conversation. There are nine celebrities in the
choice set that were chosen from three groups: three politicians, three athletes and three
movie stars. Individuals within each group are assumed to be more similar to each other
and this should have an effect on the choice probabilities beyond the variation that can
be captured by the BTL model. The data had been collected assuming that the choice
probabilities could be fully captured by a feature matrix that has unique features for each
individual plus one feature for being a politician, one feature for being an athlete and
one feature for being a movie star. The assumed feature matrix is shown in Figure 4.16.
This feature matrix can also be depicted as a tree therefore we refer to this model as
the tree EBA model (tEBA) (Tversky and Sattath, 1979).
We modeled the choice data with different specifications for the EBA model: the
model which assumes all options to have only unique features (BTL), the EBA model
with fixed features of tree structure (tEBA), two finite EBA models with the number of
features fixed to be 12 and 15 (EBA12 and EBA15), and the EBA model with infinitely
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