P
Paul Dagum
Researcher at Stanford University
Publications - 66
Citations - 4197
Paul Dagum is an academic researcher from Stanford University. The author has contributed to research in topics: Mitral valve & Mitral regurgitation. The author has an hindex of 36, co-authored 63 publications receiving 4059 citations. Previous affiliations of Paul Dagum include University of Toronto & Rockwell International.
Papers
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Journal ArticleDOI
Approximating probabilistic inference in Bayesian belief networks is NP-hard
Paul Dagum,Michael Luby +1 more
TL;DR: It is shown that the existence of a polynomial-time relative approximation algorithm for major classes of problem instances implies that NP ⊆ P is NP -hard.
Journal ArticleDOI
An Optimal Algorithm for Monte Carlo Estimation
TL;DR: An approximation algorithm is described which produces an estimate that is within a factor $1+\epsilon$ of $\mu$ with probability at least $1-\delta$ when running independent experiments with respect to any Z.
Journal ArticleDOI
Deformational Dynamics of the Aortic Root Modes and Physiologic Determinants
Paul Dagum,G. R. Green,F. J. Nistal,George T. Daughters,Tomasz A. Timek,Linda E. Foppiano,Ann F. Bolger,Neil B. Ingels,D C Miller +8 more
TL;DR: Aortic valve repair techniques or methods of replacement using unstented autograft, allograft or xenograft tissue valves that best preserve this normal pattern of aortic root dynamics should translate into a lower risk of long-term cusp deterioration.
Posted Content
Dynamic Network Models for Forecasting
TL;DR: The dynamic network model (DNM) is presented and methods for constructing, refining, and performing inference with this representation of temporal probabilistic knowledge are described, which extends static belief-network models to more general dynamic forecasting models.
Book ChapterDOI
Dynamic network models for forecasting
TL;DR: In this article, the dynamic network model (DNM) is proposed to extend static belief-network models to more general dynamic forecasting models by integrating and iteratively refining contemporaneous and time-lagged dependencies.