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Results 1 - 10
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306
Probabilistic Inference Using Markov Chain Monte Carlo Methods
, 1993
"... Probabilistic inference is an attractive approach to uncertain reasoning and empirical learning in artificial intelligence. Computational difficulties arise, however, because probabilistic models with the necessary realism and flexibility lead to complex distributions over high-dimensional spaces. R ..."
Abstract
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Cited by 736 (24 self)
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Probabilistic inference is an attractive approach to uncertain reasoning and empirical learning in artificial intelligence. Computational difficulties arise, however, because probabilistic models with the necessary realism and flexibility lead to complex distributions over high-dimensional spaces
On-the-fly Fast Mean-Field Model-Checking
, 2013
"... A novel, scalable, on-the-fly model-checking procedure is presented to verify bounded PCTL properties of selected individuals in the context of very large systems of independent interacting objects. The proposed procedure combines on-the-fly model checking techniques with deterministic mean-field a ..."
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A novel, scalable, on-the-fly model-checking procedure is presented to verify bounded PCTL properties of selected individuals in the context of very large systems of independent interacting objects. The proposed procedure combines on-the-fly model checking techniques with deterministic mean-field
Mean-Field Approach to a Probabilistic Model in Information Retrieval
"... We study an explicit parametric model of documents, queries, and relevancy assessment for Information Retrieval (IR). Mean-field methods are applied to analyze the model and derive efficient practical algorithms to estimate the parameters in the problem. The hyperparameters are estimated by a fast a ..."
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We study an explicit parametric model of documents, queries, and relevancy assessment for Information Retrieval (IR). Mean-field methods are applied to analyze the model and derive efficient practical algorithms to estimate the parameters in the problem. The hyperparameters are estimated by a fast
Approximate Probabilistic Model Checking for Programs
"... In this paper we deal with the problem of applying model checking to real programs. We verify a program without constructing the whole transition system using a technique based on Monte-Carlo sampling, also called “approximate model checking”. This technique combines model checking and randomized ap ..."
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In this paper we deal with the problem of applying model checking to real programs. We verify a program without constructing the whole transition system using a technique based on Monte-Carlo sampling, also called “approximate model checking”. This technique combines model checking and randomized
Core-Stateless Fair Queueing: A Scalable Architecture to Approximate Fair Bandwidth Allocations in High Speed Networks
, 2003
"... Router mechanisms designed to achieve fair bandwidth allocations, like Fair Queueing, have many desirable properties for congestion control in the Internet. However, such mechanisms usually need to maintain state, manage buffers, and/or perform packet scheduling on a per flow basis, and this complex ..."
Abstract
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Cited by 136 (0 self)
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packet scheduling augmented by a probabilistic dropping algorithm that uses the packet labels and an estimate of the aggregate traffic at the router. We call the scheme Core-Stateless Fair Queueing. We present simulations and analysis on the performance of this approach.
Probabilistic Abstraction for Model Checking: An Approach Based . . .
- IN PROC. 17TH IEEE SYMP. ON LOGIC IN COMPUT. SCI. (LICS 2002
, 2001
"... In model checking, program correctness on all inputs is verified by considering the transition system underlying a given program. In practice, the system can be intractably large. In property testing, a property of a single input is verified by looking at a small subset of that input. We join the ..."
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Cited by 14 (1 self)
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the strengths of both approaches by introducing to model checking the notion of probabilistic abstraction. We put forth the notion of "-reducibility which is implicit in many property testers. Our probabilistic abstraction associates a set of small random transition systems to a program. Under some
Statistical-Mechanical Approach to Probabilistic Image Processing — Loopy Belief Propagation and Advanced Mean-Field Method —
"... The framework is presented of Bayesian image restoration for multi-valued images by means of the multi-state classical spin systems. Hyperparameters in the probabilistic models are determined so as to maximize the marginal likelihood. A practical algorithm is described for multi-valued image restora ..."
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The framework is presented of Bayesian image restoration for multi-valued images by means of the multi-state classical spin systems. Hyperparameters in the probabilistic models are determined so as to maximize the marginal likelihood. A practical algorithm is described for multi-valued image
Pareto curves for probabilistic model checking
- In Proc. 10th International Symposium on Automated Technology for Verification and Analysis (ATVA’12), LNCS
, 2012
"... Abstract. Multi-objective probabilistic model checking provides a way to verify several, possibly conflicting, quantitative properties of a stochastic system. It has useful applications in controller synthesis and compositional probabilistic verification. However, existing methods are based on linea ..."
Abstract
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Cited by 10 (5 self)
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Abstract. Multi-objective probabilistic model checking provides a way to verify several, possibly conflicting, quantitative properties of a stochastic system. It has useful applications in controller synthesis and compositional probabilistic verification. However, existing methods are based
A Unified Approach to Ranking in Probabilistic Databases
"... The dramatic growth in the number of application domains that naturally generate probabilistic, uncertain data has resulted in a need for efficiently supporting complex querying and decision-making over such data. In this paper, we present a unified approach to ranking and top-k query processing in ..."
Abstract
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Cited by 62 (3 self)
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if the datasets exhibit complex correlations modeled using probabilistic and/xor trees or Markov networks. We further propose that the parameters of the ranking function be learned from user preferences, and we develop an approach to learn those parameters. Finally, we present a comprehensive experimental study
QOS WITH RELIABILITY AND SCALABILITY IN ADAPTIVE SERVICE-BASED SYSTEMS
"... Service-based systems that are dynamically composed at runtime to provide complex, adaptive functionality are currently one of the main development paradigms in software engineering. However, the Quality of Service (QoS) delivered by these systems remains an important concern, and needs to be manage ..."
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to be managed in an equally adaptive and predictable way. To address this need, we introduce a novel, tool-supported framework for the development of adaptive servicebased systems called QoSMOS (QoS Management and Optimization of Service-based systems). QoSMOS can be used to develop service-based systems
Results 1 - 10
of
306
