  Product bundling is a common selling mechanism used in online retailing. To
set profitable bundle prices, the seller needs to learn consumer preferences
from the transaction data. When customers purchase bundles or multiple
products, classical methods such as discrete choice models cannot be used to
estimate customers' valuations. In this paper, we propose an approach to learn
the distribution of consumers' valuations toward the products using bundle
sales data. The approach reduces it to an estimation problem where the samples
are censored by polyhedral regions. Using the EM algorithm and Monte Carlo
simulation, our approach can recover the distribution of consumers' valuations.
The framework allows for unobserved no-purchases and clustered market segments.
We provide theoretical results on the identifiability of the probability model
and the convergence of the EM algorithm. The performance of the approach is
also demonstrated numerically.
