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Optical implementation of probabilistic graphical models
2016 IEEE International Conference on Rebooting Computing (ICRC), 2016We are investigating the use of optics to solve highly connected graphical models by probabilistic inference, and more specifically the sum-product message passing algorithm. We are examining the fundamental limit of size and power requirement according to the best multiplexing strategy we have found.
Pierre-Alexandre Blanche +7 more
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Probabilistic Graphical Models and Their Inferences (Tutorial)
2019 IEEE 4th International Workshops on Foundations and Applications of Self* Systems (FAS*W), 2019Probabilistic graphical models are useful for modelling stochastic phenomena for doing inferences and reasoning under uncertainty. Especially, chain graph models and Bayesian networks can be used as probabilistic expert systems where inferences can be done with junction tree algorithm, etc.
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Probabilistic graphical models
2019AbstractStatistical machine learning and statistical DSP are built on the foundations of probability theory and random variables. Different techniques encode different dependency structure between these variables. This structure leads to specific algorithms for inference and estimation. Many common dependency structures emerge naturally in this way, as
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A Probabilistic Graphical Model of Quantum Systems
2010 Ninth International Conference on Machine Learning and Applications, 2010Quantum systems are promising candidates of future computing and information processing devices. In a large system, information about the quantum states and processes may be incomplete and scattered. To integrate the distributed information we propose a quantum version of probabilistic graphical models.
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Preconditioner Approximations for Probabilistic Graphical Models
2018We present a family of approximation techniques for probabilistic graphical models, based on the use of graphical preconditioners developed in the scientific computing literature. Our framework yields rigorous upper and lower bounds on event probabilities and the log partition function of undirected graphical models, using non-iterative procedures that
Pradeep Ravikumar, John D. Lafferty
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Probabilistic Graphical Model of SPECT/MRI
2011The combination of PET and SPECT with MRI is an area of active research at present time and will enable new biological and pathological analysis tools for clinical applications and pre-clinical research. Image processing and reconstruction in multi-modal PET/MRI and SPECT/MRI poses new algorithmic and computational challenges. We investigate the use of
Stefano Pedemonte +4 more
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A probabilistic graphical model for learning as search
2017 IEEE 7th Annual Computing and Communication Workshop and Conference (CCWC), 2017This work describes ongoing research on web information retrieval using queries which require prolonged browser interactions in exploring, revising, resubmitting, and studying results produced by the search engine. Over the last two decades or so, web search has improved significantly in a lot of areas, but has not made much progress at the difficult ...
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Probabilistic graphical models
Int. J. Intell. Syst., 2017José A. Gámez 0001, Antonio Salmerón
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Multi-Marginal Optimal Transport and Probabilistic Graphical Models
IEEE Transactions on Information Theory, 2021Isabel Haasler +2 more
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