Results 11 to 20 of about 136,829,320 (227)
Maximum a Posteriori Estimation by Search in Probabilistic Programs
Proceedings of the International Symposium on Combinatorial Search, 2021 We introduce an approximate search algorithm for fast maximum a posteriori probability estimation in probabilistic programs, which we call Bayesian ascent Monte Carlo (BaMC). Probabilistic programs represent probabilistic models with varying number of mutually dependent finite, countable, and continuous random variables.
Tolpin, D, Wood, F
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Maximum a Posteriori Local Histogram Estimation for Image Registration [PDF]
, 2005 Image similarity measures for registration can be considered within the general context of joint intensity histograms, which consist of bin count parameters estimated from image intensity samples. Many approaches to estimation are ML (maximum likelihood), which tends to be unstable in the presence sparse data, resulting in registration that is driven ...
Matthew Toews +2 more
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Sparse sequence recovery via a maximum a posteriori estimation
2013 IEEE International Conference on Image Processing, 2013 A maximum a posteriori (MAP) estimation algorithm is given for reconstructing sparse signals, where a part of the support, and an approximate estimate of the sparse signal are known. This method is useful, e.g., in magnetic resonance image (MRI) sequence, natural video sequences, etc, where it is required to recursively reconstruct a sequence of ...
Mashud Hyder, Kaushik Mahata
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Bayesian ODE solvers: the maximum a posteriori estimate [PDF]
Statistics and Computing, 2021 AbstractThere is a growing interest in probabilistic numerical solutions to ordinary differential equations. In this paper, the maximum a posteriori estimate is studied under the class of $$\nu $$ ν times differentiable linear time-invariant Gauss–Markov priors, which can be computed with an iterated extended Kalman ...
Filip Tronarp +2 more
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Maximum norm a posteriori error estimate for a 2d singularly perturbed semilinear reaction-diffusion problem [PDF]
, 2008 A singularly perturbed semilinear reaction-diffusion equation, posed in the unit square, is discretized on arbitrary nonuniform tensor-product meshes. We establish a second-order maximum norm a posteriori error estimate that holds true uniformly in the ...
Kopteva, Natalia
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Maximum norm a posteriori error estimation for parabolic problems using elliptic reconstructions [PDF]
, 2013 A semilinear second-order parabolic equation is considered in a regular and a singularly perturbed regime. For this equation, we give computable a posteriori error estimates in the maximum norm.
Linss, Torsten, Kopteva, Natalia
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Word Embedding as Maximum A Posteriori Estimation [PDF]
Proceedings of the AAAI Conference on Artificial Intelligence, 2019 The GloVe word embedding model relies on solving a global optimization problem, which can be reformulated as a maximum likelihood estimation problem. In this paper, we propose to generalize this approach to word embedding by considering parametrized variants of the GloVe model and incorporating priors on these parameters.
Jameel, Shoaib +4 more
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Maximum a posteriori estimators as a limit of Bayes estimators [PDF]
Mathematical Programming, 2018 Maximum a posteriori and Bayes estimators are two common methods of point estimation in Bayesian Statistics. It is commonly accepted that maximum a posteriori estimators are a limiting case of Bayes estimators with 0-1 loss. In this paper, we provide a counterexample which shows that in general this claim is false.
Robert L. Bassett, Julio Deride
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A residual based A POSTERIORI error estimator for an augmented mixed finite element method in linear elasticity [PDF]
, 2005 In this paper we develop a residual based a posteriori error analysis for an augmented mixed finite element method applied to the problem of linear elasticity in the plane. More precisely, we derive a reliable and efficient a posteriori error estimator
Heuer, N +18 more
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Worst-Case Analysis is Maximum-A-Posteriori Estimation
CoRR, 2023 The worst-case resource usage of a program can provide useful information for many software-engineering tasks, such as performance optimization and algorithmic-complexity-vulnerability discovery. This paper presents a generic, adaptive, and sound fuzzing framework, called DSE-SMC, for estimating worst-case resource usage.
Hongjun Wu, Di Wang
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