Results 41 to 50 of about 1,281,025 (295)
Hard and Soft EM in Bayesian Network Learning from Incomplete Data
Algorithms, 2020 Incomplete data are a common feature in many domains, from clinical trials to industrial applications. Bayesian networks (BNs) are often used in these domains because of their graphical and causal interpretations.
Andrea Ruggieri +3 more
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Decentralized Expectation Propagation for Semi-Blind Channel Estimation in Cell-Free Networks [PDF]
International Symposium on Information TheoryThis paper explores uplink communication in cell-free (CF) massive multiple-input multiple-output (MaMIMO) systems, employing semi-blind transmission structures to mitigate pilot contamination.
Zilu Zhao, Dirk T. M. Slock
semanticscholar +1 more source
Propagation of Quantum Expectations with Husimi Functions [PDF]
SIAM Journal on Applied Mathematics, 2013 We analyse the dynamics of expectation values of quantum observables for the time-dependent semiclassical Schrödinger equation. To benefit from the positivity of Husimi functions, we switch between observables obtained from Weyl and Anti-Wick quantization.
Johannes Keller 0003, Caroline Lasser
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Expectation propagation for Poisson data
Inverse Problems, 2019 AbstractThe Poisson distribution arises naturally when dealing with data involving counts, and it has found many applications in inverse problems and imaging. In this work, we develop an approximate Bayesian inference technique based on expectation propagation for approximating the posterior distribution formed from the Poisson likelihood function and ...
Chen Zhang, Simon Arridge, Bangti Jin
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Expectation Propagation for Likelihood-Free Inference [PDF]
Journal of the American Statistical Association, 2014 Revised version following peer ...
Barthelme, Simon, Chopin, Nicolas
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Expectation Particle Belief Propagation
CoRR, 2015 submitted to NIPS ...
Thibaut Liénart +2 more
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Predictive Ensemble Pruning by Expectation Propagation [PDF]
IEEE Transactions on Knowledge and Data Engineering, 2009 An ensemble is a group of learners that work together as a committee to solve a problem. The existing ensemble learning algorithms often generate unnecessarily large ensembles, which consume extra computational resource and may degrade the generalization performance.
Huanhuan Chen 0001 +2 more
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Compressed sensing reconstruction using expectation propagation [PDF]
Journal of Physics A: Mathematical and Theoretical, 2020 Abstract Many interesting problems in fields ranging from telecommunications to computational biology can be formalized in terms of large underdetermined systems of linear equations with additional constraints or regularizers.
Braunstein, Alfredo +3 more
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IEEE Access, 2017 This paper deals with low-complexity joint channel estimation and decoding for faster-than-Nyquist (FTN) signaling over frequency selective fading channels.
Nan Wu +3 more
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High-Efficiency Expectation Propagation Detector for High-Order Massive MIMO Systems
IEEE Access, 2019 Multiple-inputs multiple-outputs (MIMO) technology, including massive MIMO, plays an important role in modern wireless communication systems. Massive MIMO systems with high-order modulation can promote spectrum efficiency.
Guoqiang Yao +3 more
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