TY - GEN

T1 - Exploiting uniform assignments in first-order MPE

AU - Apsel, Udi

AU - Brafman, Ronen I.

PY - 2012/12/1

Y1 - 2012/12/1

N2 - The MPE (Most Probable Explanation) query plays an important role in probabilistic inference. MPE solution algorithms for probabilistic relational models essentially adapt existing belief assessment method, replacing summation with maximization. But the rich structure and symmetries captured by relational models together with the properties of the maximization operator offer an opportunity for additional simplification with potentially significant computational ramifications. Specifically, these models often have groups of variables that define symmetric distributions over some population of formulas. The maximizing choice for different elements of this group is the same. If we can realize this ahead of time, we can significantly reduce the size of the model by eliminating a potentially significant portion of random variables. This paper defines the notion of uniformly assigned and partially uniformly assigned sets of variables, shows how one can recognize these sets efficiently, and how the model can be greatly simplified once we recognize them, with little computational effort. We demonstrate the effectiveness of these ideas empirically on a number of models.

AB - The MPE (Most Probable Explanation) query plays an important role in probabilistic inference. MPE solution algorithms for probabilistic relational models essentially adapt existing belief assessment method, replacing summation with maximization. But the rich structure and symmetries captured by relational models together with the properties of the maximization operator offer an opportunity for additional simplification with potentially significant computational ramifications. Specifically, these models often have groups of variables that define symmetric distributions over some population of formulas. The maximizing choice for different elements of this group is the same. If we can realize this ahead of time, we can significantly reduce the size of the model by eliminating a potentially significant portion of random variables. This paper defines the notion of uniformly assigned and partially uniformly assigned sets of variables, shows how one can recognize these sets efficiently, and how the model can be greatly simplified once we recognize them, with little computational effort. We demonstrate the effectiveness of these ideas empirically on a number of models.

UR - http://www.scopus.com/inward/record.url?scp=84885879081&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:84885879081

SN - 9780974903989

T3 - Uncertainty in Artificial Intelligence - Proceedings of the 28th Conference, UAI 2012

SP - 74

EP - 83

BT - Uncertainty in Artificial Intelligence - Proceedings of the 28th Conference, UAI 2012

T2 - 28th Conference on Uncertainty in Artificial Intelligence, UAI 2012

Y2 - 15 August 2012 through 17 August 2012

ER -