The Experts below are selected from a list of 4815 Experts worldwide ranked by ideXlab platform
David Heckerman - One of the best experts on this subject based on the ideXlab platform.
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Inference Algorithms for Similarity Networks
arXiv: Artificial Intelligence, 2013Co-Authors: Dan Geiger, David HeckermanAbstract:We examine two types of similarity networks each based on a distinct notion of relevance. For both types of similarity networks we present an efficient inference algorithm that works under the assumption that every event has a Nonzero Probability of occurrence. Another inference algorithm is developed for type 1 similarity networks that works under no restriction, albeit less efficiently.
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knowledge representation and inference in similarity networks and bayesian multinets
Artificial Intelligence, 1996Co-Authors: Dan Geiger, David HeckermanAbstract:We examine two representation schemes for uncertain knowledge: the similarity network (Heckerman, 1991) and the Bayesian multinet. These schemes are extensions of the Bayesian network model in that they represent asymmetric independence assertions. We explicate the notion of relevance upon which similarity networks are based and present an efficient inference algorithm that works under the assumption that every event has a Nonzero Probability. Another inference algorithm is developed that works under no restriction albeit less efficiently. We show that similarity networks are not inferentially complete-namely-not every query can be answered. Nonetheless, we show that a similarity network can always answer any query of the form: “What is the posterior Probability of an hypothesis given evidence?” We call this property diagnostic completeIZESS. Finally, we describe a generalization of similarity networks that can encode more types of asymmetric conditional independence assertions than can ordinary similarity networks.
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UAI - Inference algorithms for similarity networks
Uncertainty in Artificial Intelligence, 1993Co-Authors: Dan Geiger, David HeckermanAbstract:We examine two types of similarity networks each based on a distinct notion of relevance. For both types of similarity networks we present an efficient inference algorithm that works under the assumption that every event has a Nonzero Probability of occurrence. Another inference algorithm is developed for type 1 similarity networks that works under no restriction, albeit less efficiently.
Dan Geiger - One of the best experts on this subject based on the ideXlab platform.
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Inference Algorithms for Similarity Networks
arXiv: Artificial Intelligence, 2013Co-Authors: Dan Geiger, David HeckermanAbstract:We examine two types of similarity networks each based on a distinct notion of relevance. For both types of similarity networks we present an efficient inference algorithm that works under the assumption that every event has a Nonzero Probability of occurrence. Another inference algorithm is developed for type 1 similarity networks that works under no restriction, albeit less efficiently.
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knowledge representation and inference in similarity networks and bayesian multinets
Artificial Intelligence, 1996Co-Authors: Dan Geiger, David HeckermanAbstract:We examine two representation schemes for uncertain knowledge: the similarity network (Heckerman, 1991) and the Bayesian multinet. These schemes are extensions of the Bayesian network model in that they represent asymmetric independence assertions. We explicate the notion of relevance upon which similarity networks are based and present an efficient inference algorithm that works under the assumption that every event has a Nonzero Probability. Another inference algorithm is developed that works under no restriction albeit less efficiently. We show that similarity networks are not inferentially complete-namely-not every query can be answered. Nonetheless, we show that a similarity network can always answer any query of the form: “What is the posterior Probability of an hypothesis given evidence?” We call this property diagnostic completeIZESS. Finally, we describe a generalization of similarity networks that can encode more types of asymmetric conditional independence assertions than can ordinary similarity networks.
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UAI - Inference algorithms for similarity networks
Uncertainty in Artificial Intelligence, 1993Co-Authors: Dan Geiger, David HeckermanAbstract:We examine two types of similarity networks each based on a distinct notion of relevance. For both types of similarity networks we present an efficient inference algorithm that works under the assumption that every event has a Nonzero Probability of occurrence. Another inference algorithm is developed for type 1 similarity networks that works under no restriction, albeit less efficiently.
Adrian Kent - One of the best experts on this subject based on the ideXlab platform.
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Cheat Sensitive Quantum Bit Commitment
Physical review letters, 2004Co-Authors: Lucien Hardy, Adrian KentAbstract:We define cheat sensitive cryptographic protocols between mistrustful parties as protocols which guarantee that, if either cheats, the other has some Nonzero Probability of detecting the cheating. We describe an unconditionally secure cheat sensitive nonrelativistic bit commitment protocol which uses quantum information to implement a task which is classically impossible; we also describe a simple relativistic protocol.
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Optimal Entanglement Enhancement for Mixed States
Physical Review Letters, 1999Co-Authors: Adrian Kent, Noah Linden, Serge MassarAbstract:We consider the actions of protocols involving local quantum operations and classical communication (LQCC) on a single system consisting of two separated qubits. We give a complete description of the orbits of the space of states under LQCC and characterize the representatives with maximal entanglement of formation. We thus obtain a LQCC entanglement concentration protocol for a single given state (pure or mixed) of two qubits which is optimal in the sense that the protocol produces, with Nonzero Probability, a state of maximal possible entanglement of formation. This defines a new entanglement measure, the maximum extractable entanglement.
Lucien Hardy - One of the best experts on this subject based on the ideXlab platform.
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Cheat Sensitive Quantum Bit Commitment
Physical review letters, 2004Co-Authors: Lucien Hardy, Adrian KentAbstract:We define cheat sensitive cryptographic protocols between mistrustful parties as protocols which guarantee that, if either cheats, the other has some Nonzero Probability of detecting the cheating. We describe an unconditionally secure cheat sensitive nonrelativistic bit commitment protocol which uses quantum information to implement a task which is classically impossible; we also describe a simple relativistic protocol.
Fei Gao - One of the best experts on this subject based on the ideXlab platform.
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Security of Verifiable Threshold Quantum Secret Sharing With Sequential Communication
IEEE Access, 2019Co-Authors: Xiao-qiu Cai, Tianyin Wang, Rui-ling Zhang, Fei GaoAbstract:A verifiable (t, n) threshold quantum secret sharing scheme with sequential communication was proposed recently. In this work, we analyze its security and then give two new participant attacks. Using the first participant attack, the first participant can obtain the dealer's secrets by himself with Nonzero Probability without being detected. Using the second participant attack, a dishonest participant can gain access to the dealer's secrets by himself in the secret reconstruction phase while he can make the other participants recover false secrets instead of the real ones without being detected. Furthermore, we present an effective way to prevent these attacks.
