Results 181 to 190 of about 136,829,320 (227)
A Unified Block‐Modal Framework for Inverse Source Problems in Heat and Mass Transfer
International Journal for Numerical Methods in Engineering, Volume 127, Issue 19, 15 October 2026.ABSTRACT This work presents a unified block–modal framework for inverse source identification in linear diffusive problems arising in heat and mass transfer. Starting from a general parabolic model with mixed boundary operators, the Classical Integral Transform Technique is employed to project the dynamics onto an orthonormal eigenbasis, yielding a ...
André J. P. de Oliveira +5 more
wiley +1 more source
Abstract Supplement HIV Glasgow 8–11 November 2026, Glasgow, UK/Virtual
Journal of the International AIDS Society, Volume 29, Issue S6, November 2026.
wiley +1 more source
Fast, Approximate Maximum a Posteriori Probability Parameter Estimation
IEEE Signal Processing Letters, 1997 In many estimation problems, the set of unknown parameters can be divided into a subset of desired parameters and a subset of nuisance parameters. Using a maximum a posteriori (MAP) approach to parameter estimation, these nuisance parameters are integrated out in the estimation process.
R J Vaccaro, D W Tufts
exaly +5 more sources
A Note on the Estimation of Variances in State-Space Models Using the Maximum a Posteriori Procedure
IEEE Transactions on Automatic Control, 1985 This note examines an estimation procedure for the unknown parameters in a state-space model proposed by Tsang, Glover, and Bach. The method is based on the maximum a posteriori (MAP) principle. Contrary to the claims of Tsang et al. it is shown that the
Peters, Simon; id_orcid, Harvey, Andrew
exaly +9 more sources
Revisiting Maximum-a-Posteriori Estimation in Log-Concave Models [PDF]
SIAM Journal on Imaging Sciences, 2019 Maximum-a-posteriori (MAP) estimation is the main Bayesian estimation methodology in imaging sciences, where high dimensionality is often addressed by using Bayesian models that are log-concave and whose posterior mode can be computed efficiently by convex optimization. However, despite its success and wide adoption, MAP estimation is not theoretically
Marcelo Pereyra
exaly +4 more sources
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Maximum a Posteriori Estimation of Change Points in the EEG
International Journal of Bio-medical Computing, 1995A new approach for EEG segmentation is introduced. This is based on a methodology for optimal segmentation of non-stationary signals derived from the maximum a posteriori estimation principle. It is a model-based, not sequential approach that allows for segmentation at different resolution levels.
Marc Lavielle, Pedro Valdés, P Valdés
exaly +3 more sources
Neural Maximum a Posteriori Estimation on Unpaired Data for Motion Deblurring
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023 Real-world dynamic scene deblurring has long been a challenging task since paired blurry-sharp training data is unavailable. Conventional Maximum A Posteriori estimation and deep learning-based deblurring methods are restricted by handcrafted priors and synthetic blurry-sharp training pairs respectively, thereby failing to generalize to real dynamic ...
Dacheng Tao, Chaoyue Wang, Youjian Zhang
exaly +4 more sources
Maximum a posteriori estimation of signal rank
2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014Estimating the number of linearly independent signals impinging on a collection of receivers is of increasing recent interest, particularly in connection with MIMO systems for communications and sensing. This paper derives maximum a posteriori estimators for signal rank from noisy data collected at multiple receivers. Situations in which noise variance
Songsri Sirianunpiboon +2 more
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Maximum a posteriori height estimation in InSAR imaging
IEEE International Geoscience and Remote Sensing Symposium, 2003A multi-frequencies maximum a posteriori (MAP) estimation of height profiles, from InSAR data, is presented in this paper. A quadratic MRF model is adopted to exploit a-priori information about the unknown image; the hyperparameter estimation, performed on a local basis, provides a powerful representation model for realistic height surfaces.
Giancarlo Ferraiuolo +2 more
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A recursive maximum a posteriori estimator
Asian Journal of Control, 2010Summary: Kalman Filter (KF) is the optimal state estimator for linear dynamical systems in the presence of zero mean white Gaussian noise. It is a Minimum Mean Square Error (MMSE) estimator. In the present work a Recursive Maximum A Posteriori Estimator (RMAPE) has been developed from basic principles of estimation.
Mukherjee, Arpita, Sengupta, Aparajita
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