Open Access
The bayes net toolbox for matlab
K. Murphy
- Vol. 33
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TLDR
A broad spectrum of issues related to graphical models (directed and undirected) are discussed, and how BNT was designed to cope with them all are described, at a high-level.Abstract:
The Bayes Net Toolbox (BNT) is an open-source Matlab package for directed graphical models. BNT supports many kinds of nodes (probability distributions), exact and approximate inference, parameter and structure learning, and static and dynamic models. BNT is widely used in teaching and research: the web page has received over 28,000 hits since May 2000. In this paper, we discuss a broad spectrum of issues related to graphical models (directed and undirected), and describe, at a high-level, how BNT was designed to cope with them all. We also compare BNT to other software packages for graphical models, and discuss the nascent OpenBayes e ort.read more
Citations
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Dynamic bayesian networks: representation, inference and learning
Kevin Murphy,Stuart Russell +1 more
TL;DR: This thesis will discuss how to represent many different kinds of models as DBNs, how to perform exact and approximate inference in Dbns, and how to learn DBN models from sequential data.
Journal ArticleDOI
Putting Objects in Perspective
TL;DR: This paper provides a framework for placing local object detection in the context of the overall 3D scene by modeling the interdependence of objects, surface orientations, and camera viewpoint by allowing probabilistic object hypotheses to refine geometry and vice-versa.
Journal ArticleDOI
SATzilla: portfolio-based algorithm selection for SAT
TL;DR: SATzilla is described, an automated approach for constructing per-instance algorithm portfolios for SAT that use so-called empirical hardness models to choose among their constituent solvers and is improved by integrating local search solvers as candidate solvers, by predicting performance score instead of runtime, and by using hierarchical hardness models that take into account different types of SAT instances.
Journal ArticleDOI
How to infer gene networks from expression profiles
TL;DR: It is shown that reverse‐engineering algorithms are indeed able to correctly infer regulatory interactions among genes, at least when one performs perturbation experiments complying with the algorithm requirements.
Proceedings ArticleDOI
Depth and Appearance for Mobile Scene Analysis
TL;DR: This work proposes a novel iterative approach that first infers scene geometry using belief propagation and then resolves interactions between objects using a global optimization procedure, which leads to a robust solution in few iterations, while allowing object detection to benefit from geometry estimation and vice versa.
References
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MonographDOI
Causality: models, reasoning, and inference
TL;DR: The art and science of cause and effect have been studied in the social sciences for a long time as mentioned in this paper, see, e.g., the theory of inferred causation, causal diagrams and the identification of causal effects.
Journal ArticleDOI
Factor graphs and the sum-product algorithm
TL;DR: A generic message-passing algorithm, the sum-product algorithm, that operates in a factor graph, that computes-either exactly or approximately-various marginal functions derived from the global function.
Book
Causation, prediction, and search
TL;DR: The authors axiomatize the connection between causal structure and probabilistic independence, explore several varieties of causal indistinguishability, formulate a theory of manipulation, and develop asymptotically reliable procedures for searching over equivalence classes of causal models.