506 citations · 1.3k across the 28 of their papers we have counts for
6 papers · 1 filter
Toward a Market Model for Bayesian Inference
David M. Pennock, Michael P. Wellman
We present a methodology for representing probabilistic relationships in a general-equilibrium economic model. Specifically, we define a precise mapping from a Bayesian network wit…
Representing Aggregate Belief through the Competitive Equilibrium of a Securities Market
David M. Pennock, Michael P. Wellman
We consider the problem of belief aggregation: given a group of individual agents with probabilistic beliefs over a set of uncertain events, formulate a sensible consensus or aggre…
Logarithmic Time Parallel Bayesian Inference
David M. Pennock
I present a parallel algorithm for exact probabilistic inference in Bayesian networks. For polytree networks with n variables, the worst-case time complexity is O(log n) on a CREW…
Graphical Representations of Consensus Belief
David M. Pennock, Michael P. Wellman
Graphical models based on conditional independence support concise encodings of the subjective belief of a single agent. A natural question is whether the consensus belief of a gro…
Collaborative Filtering by Personality Diagnosis: A Hybrid Memory- and Model-Based Approach
David M. Pennock, Eric J. Horvitz, Steve Lawrence +1
The growth of Internet commerce has stimulated the use of collaborative filtering (CF) algorithms as recommender systems. Such systems leverage knowledge about the known preference…
Probabilistic Models for Unified Collaborative and Content-Based Recommendation in Sparse-Data Environments
Alexandrin Popescul, Lyle H. Ungar, David M Pennock +1
Recommender systems leverage product and community information to target products to consumers. Researchers have developed collaborative recommenders, content-based recommenders, a…