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We report the orbital distribution of the trans-neptunian objects (TNOs) discovered during the Canada-France Ecliptic Plane Survey, whose discovery phase ran from early 2003 until early 2007. The follow-up observations started just after the first discoveries and extended until late 2009. We obtained characterized observations of 321 sq.deg. of sky to depths in the range g ~ 23.5--24.4 AB mag. We provide a database of 169 TNOs with high-precision dynamical classification and known discovery efficiency. Using this database, we find that the classical belt is a complex region with sub-structures that go beyond the usual splitting of inner (interior to 3:2 mean-motion resonance [MMR]), outer (exterior to 2:1 MMR), and main (in between). The main classical belt (a=40--47 AU) needs to be modeled with at least three components: the `hot' component with a wide inclination distribution and two `cold' components (stirred and kernel) with much narrower inclination distributions. The hot component must have a significantly shallower absolute magnitude (Hg) distribution than the other two components. With 95% confidence, there are 8000+1800-1600 objects in the main belt with Hg, 59 pages, 9 figures, 7 tables

Interactive dynamic influence diagrams (I-DIDs) are graphical models for sequential decision making in uncertain settings shared by other agents. Algorithms for solving I-DIDs face the challenge of an exponentially growing space of behavioral models ascribed to other agents over time. Previous approaches mainly cluster behaviorally equivalent models to reduce the complexity of I-DID solutions. In this paper, we seek to further reduce the model space by introducing an approximate measure of behavioral equivalence (BE) and using it to group models. Specifically, we focus on $K$ most probable paths in the solution of each model and compare these policy paths to determine approximate BE. We discuss the challenges in computing the top $K$ policy paths and experimentally evaluate the performance of this heuristic approach in terms of the scalability and quality of the solution. Interactive dynamic influence diagrams (I-DIDs) are graphical models for sequential decision making in uncertain settings shared by other agents. Algorithms for solving I-DIDs face the challenge of an exponentially growing space of behavioral models ascribed to other agents over time. Previous approaches mainly cluster behaviorally equivalent models to reduce the complexity of I-DID solutions. In this paper, we seek to further reduce the model space by introducing an approximate measure of behavioral equivalence (BE) and using it to group models. Specifically, we focus on K most probable paths in the solution of each model and compare these policy paths to determine approximate BE. We discuss the challenges in computing the top K policy paths and experimentally evaluate the performance of this heuristic approach in terms of the scalability and quality of the solution.