The Experts below are selected from a list of 204 Experts worldwide ranked by ideXlab platform
Gregory Provan - One of the best experts on this subject based on the ideXlab platform.
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query dags a practical paradigm for implementing belief network inference
arXiv: Artificial Intelligence, 2014Co-Authors: Adnan Darwiche, Gregory ProvanAbstract:We describe a new paradigm for implementing inference in belief networks, which relies on compiling a belief network into an Arithmetic Expression called a Query DAG (Q-DAG). Each non-leaf node of a Q-DAG represents a numeric operation, a number, or a symbol for evidence. Each leaf node of a Q-DAG represents the answer to a network query, that is, the probability of some event of interest. It appears that Q-DAGs can be generated using any of the algorithms for exact inference in belief networks --- we show how they can be generated using clustering and conditioning algorithms. The time and space complexity of a Q-DAG generation algorithm is no worse than the time complexity of the inference algorithm on which it is based; that of a Q-DAG on-line evaluation algorithm is linear in the size of the Q-DAG, and such inference amounts to a standard evaluation of the Arithmetic Expression it represents. The main value of Q-DAGs is in reducing the software and hardware resources required to utilize belief networks in on-line, real-world applications. The proposed framework also facilitates the development of on-line inference on different software and hardware platforms, given the simplicity of the Q-DAG evaluation algorithm. This paper describes this new paradigm for probabilistic inference, explaining how it works, its uses, and outlines some of the research directions that it leads to.
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query dags a practical paradigm for implementing belief network inference
Uncertainty in Artificial Intelligence, 1996Co-Authors: Adnan Darwiche, Gregory ProvanAbstract:We describe a new paradigm for implementing inference in belief networks, which consists of two steps: (1) compiling a belief network into an Arithmetic Expression called a Query DAG (Q-DAG); and (2) answering queries using a simple evaluation algorithm. Each non-leaf node of a Q-DAG represents a numeric operation, a number, or a symbol for evidence. Each leaf node of a Q-DAG represents the answer to a network query, that is, the probability of some event of interest. It appears that Q-DAGs can be generated using any of the standard algorithms for exact inference in belief networks -- we show how they can be generated using the clustering algorithm. The time and space complexity of a Q-DAG generation algorithm is no worse than the time complexity of the inference algorithm on which it is based. The complexity of a Q-DAG evaluation algorithm is linear in the size of the Q-DAG, and such inference amounts to a standard evaluation of the Arithmetic Expression it represents. The main value of Q-DAGs is in reducing the software and hardware resources required to utilize belief networks in on-line, real-world applications. The proposed framework also facilitates the development of on-line inference on different software and hardware platforms due to the simplicity of the Q-DAG evaluation algorithm.
Connie Rash - One of the best experts on this subject based on the ideXlab platform.
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Haptic Human-Computer Interaction - Haptic Display of Mathematical Functions for Teaching Mathematics to Students with Vision Disabilities: Design and Proof of Concept
Haptic Human-Computer Interaction, 2001Co-Authors: Frances L. Van Scoy, Takamitsu Kawai, Marjorie Darrah, Connie RashAbstract:The design and initial implementation of a system for constructing a haptic model of a mathematical function for exploration using a PHANToM are described. A user types the mathematical function as a Fortran Arithmetic Expression and the system described here carves the trace of the function onto a virtual block of balsa wood. Preliminary work in generating music which describes the function has begun.
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Haptic display of mathematical functions for teaching mathematics to students with vision disabilities: design and proof of concept
Lecture Notes in Computer Science, 2001Co-Authors: Frances L. Van Scoy, Takamitsu Kawai, Marjorie Darrah, Connie RashAbstract:The design and initial implementation of a system for constructing a haptic model of a mathematical function for exploration using a PHANToM are described. A user types the mathematical function as a Fortran Arithmetic Expression and the system described here carves the trace of the function onto a virtual block of balsa wood. Preliminary work in generating music which describes the function has begun.
Adnan Darwiche - One of the best experts on this subject based on the ideXlab platform.
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query dags a practical paradigm for implementing belief network inference
arXiv: Artificial Intelligence, 2014Co-Authors: Adnan Darwiche, Gregory ProvanAbstract:We describe a new paradigm for implementing inference in belief networks, which relies on compiling a belief network into an Arithmetic Expression called a Query DAG (Q-DAG). Each non-leaf node of a Q-DAG represents a numeric operation, a number, or a symbol for evidence. Each leaf node of a Q-DAG represents the answer to a network query, that is, the probability of some event of interest. It appears that Q-DAGs can be generated using any of the algorithms for exact inference in belief networks --- we show how they can be generated using clustering and conditioning algorithms. The time and space complexity of a Q-DAG generation algorithm is no worse than the time complexity of the inference algorithm on which it is based; that of a Q-DAG on-line evaluation algorithm is linear in the size of the Q-DAG, and such inference amounts to a standard evaluation of the Arithmetic Expression it represents. The main value of Q-DAGs is in reducing the software and hardware resources required to utilize belief networks in on-line, real-world applications. The proposed framework also facilitates the development of on-line inference on different software and hardware platforms, given the simplicity of the Q-DAG evaluation algorithm. This paper describes this new paradigm for probabilistic inference, explaining how it works, its uses, and outlines some of the research directions that it leads to.
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query dags a practical paradigm for implementing belief network inference
Uncertainty in Artificial Intelligence, 1996Co-Authors: Adnan Darwiche, Gregory ProvanAbstract:We describe a new paradigm for implementing inference in belief networks, which consists of two steps: (1) compiling a belief network into an Arithmetic Expression called a Query DAG (Q-DAG); and (2) answering queries using a simple evaluation algorithm. Each non-leaf node of a Q-DAG represents a numeric operation, a number, or a symbol for evidence. Each leaf node of a Q-DAG represents the answer to a network query, that is, the probability of some event of interest. It appears that Q-DAGs can be generated using any of the standard algorithms for exact inference in belief networks -- we show how they can be generated using the clustering algorithm. The time and space complexity of a Q-DAG generation algorithm is no worse than the time complexity of the inference algorithm on which it is based. The complexity of a Q-DAG evaluation algorithm is linear in the size of the Q-DAG, and such inference amounts to a standard evaluation of the Arithmetic Expression it represents. The main value of Q-DAGs is in reducing the software and hardware resources required to utilize belief networks in on-line, real-world applications. The proposed framework also facilitates the development of on-line inference on different software and hardware platforms due to the simplicity of the Q-DAG evaluation algorithm.
Frances L. Van Scoy - One of the best experts on this subject based on the ideXlab platform.
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Haptic Human-Computer Interaction - Haptic Display of Mathematical Functions for Teaching Mathematics to Students with Vision Disabilities: Design and Proof of Concept
Haptic Human-Computer Interaction, 2001Co-Authors: Frances L. Van Scoy, Takamitsu Kawai, Marjorie Darrah, Connie RashAbstract:The design and initial implementation of a system for constructing a haptic model of a mathematical function for exploration using a PHANToM are described. A user types the mathematical function as a Fortran Arithmetic Expression and the system described here carves the trace of the function onto a virtual block of balsa wood. Preliminary work in generating music which describes the function has begun.
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Haptic display of mathematical functions for teaching mathematics to students with vision disabilities: design and proof of concept
Lecture Notes in Computer Science, 2001Co-Authors: Frances L. Van Scoy, Takamitsu Kawai, Marjorie Darrah, Connie RashAbstract:The design and initial implementation of a system for constructing a haptic model of a mathematical function for exploration using a PHANToM are described. A user types the mathematical function as a Fortran Arithmetic Expression and the system described here carves the trace of the function onto a virtual block of balsa wood. Preliminary work in generating music which describes the function has begun.
Marjorie Darrah - One of the best experts on this subject based on the ideXlab platform.
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Haptic Human-Computer Interaction - Haptic Display of Mathematical Functions for Teaching Mathematics to Students with Vision Disabilities: Design and Proof of Concept
Haptic Human-Computer Interaction, 2001Co-Authors: Frances L. Van Scoy, Takamitsu Kawai, Marjorie Darrah, Connie RashAbstract:The design and initial implementation of a system for constructing a haptic model of a mathematical function for exploration using a PHANToM are described. A user types the mathematical function as a Fortran Arithmetic Expression and the system described here carves the trace of the function onto a virtual block of balsa wood. Preliminary work in generating music which describes the function has begun.
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Haptic display of mathematical functions for teaching mathematics to students with vision disabilities: design and proof of concept
Lecture Notes in Computer Science, 2001Co-Authors: Frances L. Van Scoy, Takamitsu Kawai, Marjorie Darrah, Connie RashAbstract:The design and initial implementation of a system for constructing a haptic model of a mathematical function for exploration using a PHANToM are described. A user types the mathematical function as a Fortran Arithmetic Expression and the system described here carves the trace of the function onto a virtual block of balsa wood. Preliminary work in generating music which describes the function has begun.