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Maximum a posteriori estimation

About: Maximum a posteriori estimation is a research topic. Over the lifetime, 7486 publications have been published within this topic receiving 222291 citations. The topic is also known as: Maximum a posteriori, MAP & maximum a posteriori probability.


Papers
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Journal ArticleDOI
TL;DR: A robust integrated positioning method to provide ground vehicles in urban environments with accurate and reliable localization results is described, with a new reliability factor that can indicate the satellite faults effectively and provide successful positioning despite contaminated observations.

45 citations

Journal ArticleDOI
TL;DR: Compared with the OSEM and 3DRP algorithms, MRP provides comparable or better results depending on the parameters used for the reconstruction of the images, with good preservation of spatial details.
Abstract: A fully three-dimensional (3D) one-step late (OSL), maximum a posteriori (MAP) reconstruction algorithm based on the median root prior (MRP) was implemented and evaluated for the reconstruction of 3D positron emission tomography (PET) studies. The algorithm uses the ordered subsets (OS) scheme for convergence acceleration and data update during iterations. The algorithm was implemented using the software package developed within the EU project PARAPET (www.brunel.ac.uk/~masrppet). The MRP algorithm was evaluated using experimental phantom and real 3D PET brain studies. Various experimental set-ups in terms of activity distribution and counting statistics were considered. The performance of the algorithm was assessed by calculating figures of merit such as: contrast, coefficient of variation, activity ratio between two regions and full width at half of maximum for resolution measurements. The performance of MRP was compared with that of 3D ordered subsets-expectation maximisation (OSEM) and 3D re-projection (3DRP) algorithms. In all the experimental situations considered, MRP showed: (1) convergence to a stable solution, (2) effectiveness in noise reduction, particularly for low statistics data, (3) good preservation of spatial details. Compared with the OSEM and 3DRP algorithms, MRP provides comparable or better results depending on the parameters used for the reconstruction of the images.

45 citations

Proceedings ArticleDOI
09 May 1995
TL;DR: The paper presents a fast and incremental speaker adaptation method called MAP/VFS, which combines maximum a posteriori (MAP) estimation, or in other words Bayesian learning, with vector field smoothing (VFS).
Abstract: The paper presents a fast and incremental speaker adaptation method called MAP/VFS, which combines maximum a posteriori (MAP) estimation, or in other words Bayesian learning, with vector field smoothing (VFS). The point is that MAP is an intra-class training scheme while VFS is an inter-class smoothing technique. This is a basic technique for on-line adaptation which will be important in constructing a practical speech recognition system. Speaker adaptation speed of the incremental MAP is experimentally shown to be significantly accelerated by the use of VFS in word-by-word adaptation. The recognition performance of MAP is consistently improved and stabilized by VFS. The word error reduction rate achieved in incrementally adapting a few words of sample data is about 22%.

45 citations

Proceedings ArticleDOI
01 Nov 1989
TL;DR: A Markov random field model-based approach to automated image interpretation is described and demonstrated as a region-based scheme and provides a systematic method for organizing and representing domain knowledge through the clique functions of the pdf of the underlying MRF.
Abstract: In this paper, a Markov random field (MRF) model-based approach to automated image interpretation is described and demonstrated as a region-based scheme. In this approach, an image is first segmented into a collection of disjoint regions which form the nodes of an adjacency graph. Image interpretation is then achieved through assigning object labels, or interpretations, to the segmented regions, or nodes, using domain knowledge, extracted feature measurements and spatial relationships between the various regions. The interpretation labels are modeled as a MRF on the corresponding adjacency graph and the image interpretation problem is formulated as a maximum a posteriori (MAP) estimation rule. Simulated annealing is used to find the best realization, or optimal MAP interpretation. Through the MRF model, this approach also provides a systematic method for organizing and representing domain knowledge through the clique functions of the pdf of the underlying MRF. Results of image interpretation experiments performed on synthetic and real-world images using this approach are described and appear promising.

45 citations

Journal ArticleDOI
TL;DR: Monte Carlo simulation results suggest that MAP mode linking can potentially provide a significant improvement in ground track accuracy over conventional mode linking with higher probabilities of correct track associations and ray mode assignments.
Abstract: Over-the-horizon (OTH) radar exploits the refractive nature of high-frequency radio-wave propagation through the ionosphere for the purpose of wide-area surveillance. In order to localize targets, however, multipath slant tracks from different ionospheric layers, but the same target must be combined. The process of track association is complicated both by uncertainty in downrange ionospheric conditions and by the fact that in multiple target cases, the associations of slant tracks to targets are unknown. This paper proposes a method for joint multiple target ground track estimation and slant track association, or mode linking, with uncertain ionospheric conditions where the slant-track-to-target assignments and slant tracks' ray mode paths are unknown. Maximum a posteriori (MAP) mode linking exploits the statistical dependence between slant tracks on different ray mode paths to provide accurate mode linking decisions and ray path assignments and, thus, accurate ground track estimates. The approach uses Markov modeling for the dependence between different ray path types as well as for the temporal correlation between mode linking hypotheses at different revisits to obtain consistent mode linking decisions. Monte Carlo simulation results suggest that MAP mode linking can potentially provide a significant improvement in ground track accuracy over conventional mode linking with higher probabilities of correct track associations and ray mode assignments. Results with real OTH radar slant track data of multiple slant tracks from multiple targets and validated against ground truth that support the simulation study are presented.

45 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202364
2022125
2021211
2020244
2019250
2018236