Maximum a posteriori estimation

In Bayesian statistics, a maximum a posteriori probability (MAP) estimate is a mode of the posterior distribution. (Wikipedia.org)






Conferences related to Maximum a posteriori estimation

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2023 Annual International Conference of the IEEE Engineering in Medicine & Biology Conference (EMBC)

The conference program will consist of plenary lectures, symposia, workshops and invitedsessions of the latest significant findings and developments in all the major fields of biomedical engineering.Submitted full papers will be peer reviewed. Accepted high quality papers will be presented in oral and poster sessions,will appear in the Conference Proceedings and will be indexed in PubMed/MEDLINE.


2020 American Control Conference (ACC)

The ACC is the annual conference of the American Automatic Control Council (AACC, the U.S. national member organization of the International Federation for Automatic Control (IFAC)). The ACC is internationally recognized as a premier scientific and engineering conference dedicated to the advancement of control theory and practice. The ACC brings together an international community of researchers and practitioners to discuss the latest findings in automatic control. The 2020 ACC technical program will

  • 1996 13th Triennial World Congress of the International Federation of Automatic Control (IFAC)

  • 1997 American Control Conference - ACC '97

  • 1998 American Control Conference - ACC '98

  • 1999 American Control Conference - ACC '99

  • 2000 American Control Conference - ACC 2000

  • 2001 American Control Conference - ACC 2001

  • 2002 American Control Conference - ACC 2002

  • 2003 American Control Conference - ACC 2003

  • 2004 American Control Conference - ACC 2004

  • 2005 American Control Conference - ACC 2005

  • 2006 American Control Conference - ACC 2006 (Silver Anniversary)

  • 2007 American Control Conference - ACC 2007

  • 2008 American Control Conference - ACC 2008

  • 2009 American Control Conference - ACC 2009

    The 2009 ACC technical program will cover new developments related to theory, application, and education in control science and engineering. In addition to regular technical sessions the program will also feature interactive and tutorial sessions and preconference workshops.

  • 2010 American Control Conference - ACC 2010

    Theory and practice of automatic control

  • 2011 American Control Conference - ACC 2011

    ACC provides a forum for bringing industry and academia together to discuss the latest developments in the area of Automatic Control Systems, from new control theories, to the advances in sensors and actuator technologies, and to new applications areas for automation.

  • 2012 American Control Conference - ACC 2012

    All areas of control engineering and science.

  • 2013 American Control Conference (ACC)

    Control systems theory and practice. Conference themes on sustainability, societal challenges for control, smart healthcare systems. Conference topics include biological systems, vehicle dynamics and control, consensus control, cooperative control, control of communication networks, control of networked systems, control of distributed parameter systems, decentralized control, delay systems, discrete-event systems, fault detection, fault-tolerant systems, flexible structures, flight control, formation flying, fuzzy systems, hybrid systems, system identification, iterative learning control, model predictive control, linear parameter-varying systems, linear matrix inequalities, machine learning, manufacturing systems, robotics, multi-agent systems, neural networks, nonlinear control, observers, optimal control, optimization, path planning, navigation, robust control, sensor fusion, sliding mode control, stochastic systems, switched systems, uncertain systems, game theory.

  • 2014 American Control Conference - ACC 2014

    All areas of the theory and practice of automatic control, including but not limited to network control systems, model predictive control, systems analysis in biology and medicine, hybrid and switched systems, aerospace systems, power and energy systems and control of nano- and micro-systems.

  • 2015 American Control Conference (ACC)

    control theory, technology, and practice

  • 2016 American Control Conference (ACC)

    Control systems theory and practice. Conference topics include biological systems, vehicle dynamics and control, consensus control, cooperative control, control of communication networks, control of networked systems, control of distributed parameter systems, decentralized control, delay systems, discrete-event systems, fault detection, fault-tolerant systems, flexible structures, flight control, formation flying, fuzzy systems, hybrid systems, system identification, iterative learning control, model predictive control, linear parameter-varying systems, linear matrix inequalities, machine learning, manufacturing systems, robotics, multi-agent systems, neural networks, nonlinear control, observers, optimal control, optimization, path planning, navigation, robust control, sensor fusion, sliding mode control, stochastic systems, switched systems, uncertain systems, game theory.

  • 2017 American Control Conference (ACC)

    Technical topics include biological systems, vehicle dynamics and control, adaptive control, consensus control, cooperative control, control of communication networks, control of networked systems, control of distributed parameter systems, decentralized control, delay systems, discrete-event systems, fault detection, fault-tolerant systems, flexible structures, flight control, formation flying, fuzzy systems, hybrid systems, system identification, iterative learning control, model predictive control, linear parameter-varying systems, linear matrix inequalities, machine learning, manufacturing systems, robotics, multi-agent systems, neural networks, nonlinear control, observers, optimal control, optimization, path planning, navigation, robust control, sensor fusion, sliding mode control, stochastic systems, switched systems, uncertain systems, game theory.

  • 2018 Annual American Control Conference (ACC)

    Technical topics include biological systems, vehicle dynamics and control, adaptive control, consensus control, cooperative control, control of communication networks, control of networked systems, control of distributed parameter systems, decentralized control, delay systems, discrete-event systems, fault detection, fault-tolerant systems, flexible structures, flight control, formation flying, fuzzy systems, hybrid systems, system identification, iterative learning control, model predictive control, linear parameter-varying systems, linear matrix inequalities, machine learning, manufacturing systems, robotics, multi-agent systems, neural networks, nonlinear control, observers, optimal control, optimization, path planning, navigation, robust control, sensor fusion, sliding mode control, stochastic systems, switched systems, uncertain systems, game theory.

  • 2019 American Control Conference (ACC)

    Technical topics include biological systems, vehicle dynamics and control, adaptive control, consensus control, cooperative control, control of communication networks, control of networked systems, control of distributed parameter systems, decentralized control, delay systems, discrete-event systems, fault detection, fault-tolerant systems, flexible structures, flight control, formation flying, fuzzy systems, hybrid systems, system identification, iterative learning control, model predictive control, linear parameter-varying systems, linear matrix inequalities, machine learning, manufacturing systems, robotics, multi-agent systems, neural networks, nonlinear control, observers, optimal control, optimization, path planning, navigation, robust control, sensor fusion, sliding mode control, stochastic systems, switched systems, uncertain systems, game theory.


2020 IEEE International Conference on Image Processing (ICIP)

The International Conference on Image Processing (ICIP), sponsored by the IEEE SignalProcessing Society, is the premier forum for the presentation of technological advances andresearch results in the fields of theoretical, experimental, and applied image and videoprocessing. ICIP 2020, the 27th in the series that has been held annually since 1994, bringstogether leading engineers and scientists in image and video processing from around the world.


2020 IEEE Radar Conference (RadarConf20)

Annual IEEE Radar Conference

  • 1996 IEEE Radar Conference

  • 1997 IEEE Radar Conference

  • 1998 IEEE Radar Conference

  • 1999 IEEE Radar Conference

  • 2000 IEEE International Radar Conference

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  • 2004 IEEE Radar Conference

  • 2005 IEEE International Radar Conference

  • 2006 IEEE Radar Conference

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  • 2008 IEEE Radar Conference

    The 2008 IEEE Radar Conference will focus on the key aspects of radar theory and applications as listed below. Exploration of new avenues and methodologies of radar signal processing will also be encouraged. Tutorials will be held in a number of fields of radar technology. The Conference will cover all aspects of radar systems for civil, security and defense applications.

  • 2009 IEEE Radar Conference

    The conference's scope is civil and military radar, including science, technology, and systems. The theme for RADAR '09 is "Radar: From Science to Systems," emphasizing scientific or observational requirements and phenomenology that lead to the systems that we in the radar community develop.

  • 2010 IEEE International Radar Conference

    RADAR Systems, RADAR technology

  • 2011 IEEE Radar Conference (RadarCon)

    RadarCon11 will feature topics in radar systems, technology, applications, phenomenology,modeling, & signal processing. The conference theme, In the Eye of the Storm, highlights the strong regional interest in radar for severe weather analysis and tracking. Broader implications of the theme reflect global interests such as radar's role in assessing climate change, supporting myriad defense applications, as well as issues with spectrum allocation and management.

  • 2012 IEEE Radar Conference (RadarCon)

    The 2012 IEEE Radar Conference will host 400 to 600 attendees interested in innovations and developments in radar technology. The radar related topics will include presentations describing developments in radar systems and their implementations, phenomenology, target and clutter modeling, component advances, signal processing and data processing utilizing advanced algorithms. The conference will also include exhibits by vendors of radar systems, radar components, instrumentation, related software and publ

  • 2013 IEEE Radar Conference (RadarCon)

    The conference theme is The Arctic The New Frontier as it presents a vast and challenging environment for which radar systems operating in a multi-sensor environment are currently being developed for deployment on space, air, ship and ground platforms and for both remote sensing of the environment and for the monitoring of human activity. It is one of the major challenges and applications being pursued in the field of radar development in Canada.

  • 2014 IEEE Radar Conference (RadarCon)

    The 2014 IEEE Radar Conference will showcase innovations and developments in radar technology. Topics will include presentations describing developments in radar systems and their implementations, phenomenology, target and clutter modeling, signal processing, component advances, etc.

  • 2015 IEEE Radar Conference (RadarCon)

    The scope of the IEEE 2015 International Radar Conference includes all aspects of civil and military radar. Topics range from fundamental theory to cutting-edge applications, from signal processing, modeling, simulation to hardware implementation and experimental results.

  • 2016 IEEE Radar Conference (RadarCon)

    A continuing series of annual RADAR Conferences IEEE-AESS

  • 2017 IEEE Radar Conference (RadarCon)

    This conference will be a continuation of the annual IEEE radar series (formerly the IEEE National Radar conference). These conferences cover the many disciplines that span the applications of modern radar systems. This includes systems-level through subsystem and component technologies, antennas, and signal processing (deterministic and adaptive). The scope includes systems architectures of monostatic, bistatic and multistatic, and ground-based, airborne, and spaceborn realizations.

  • 2018 IEEE Radar Conference (RadarConf18)

    This conference will be a continuation of the annual IEEE radar series (formerly the IEEE National Radar conference). These conferences cover the many disciplines that span the applications of modern radar systems, including systems-level through subsystem and component technologies, antennas, and signal processing (deterministic and adaptive). The scope includes systems architectures of monostatic, bistatic and multistatic, and ground-based, airborne, shipborne, and spaceborne realizations.


2020 IEEE International Conference on Robotics and Automation (ICRA)

The International Conference on Robotics and Automation (ICRA) is the IEEE Robotics and Automation Society’s biggest conference and one of the leading international forums for robotics researchers to present their work.



Periodicals related to Maximum a posteriori estimation

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Antennas and Propagation, IEEE Transactions on

Experimental and theoretical advances in antennas including design and development, and in the propagation of electromagnetic waves including scattering, diffraction and interaction with continuous media; and applications pertinent to antennas and propagation, such as remote sensing, applied optics, and millimeter and submillimeter wave techniques.


Audio, Speech, and Language Processing, IEEE Transactions on

Speech analysis, synthesis, coding speech recognition, speaker recognition, language modeling, speech production and perception, speech enhancement. In audio, transducers, room acoustics, active sound control, human audition, analysis/synthesis/coding of music, and consumer audio. (8) (IEEE Guide for Authors) The scope for the proposed transactions includes SPEECH PROCESSING - Transmission and storage of Speech signals; speech coding; speech enhancement and noise reduction; ...


Automatic Control, IEEE Transactions on

The theory, design and application of Control Systems. It shall encompass components, and the integration of these components, as are necessary for the construction of such systems. The word `systems' as used herein shall be interpreted to include physical, biological, organizational and other entities and combinations thereof, which can be represented through a mathematical symbolism. The Field of Interest: shall ...


Biomedical Engineering, IEEE Transactions on

Broad coverage of concepts and methods of the physical and engineering sciences applied in biology and medicine, ranging from formalized mathematical theory through experimental science and technological development to practical clinical applications.


Broadcasting, IEEE Transactions on

Broadcast technology, including devices, equipment, techniques, and systems related to broadcast technology, including the production, distribution, transmission, and propagation aspects.



Most published Xplore authors for Maximum a posteriori estimation

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Xplore Articles related to Maximum a posteriori estimation

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Maximum a posteriori state estimation: a neural processing algorithm

Proceedings of the 28th IEEE Conference on Decision and Control,, 1989

A computational algorithm is presented for obtaining the maximum a posteriori estimates of the states of a stochastic dynamical system by programming a neural network. It is well known that for real-time control implementations, especially in such applications as multitarget tracking and vision-guided robots, the computational requirements for solving such state estimation problems attain particular significance, and parallel processing techniques ...


Model Order Selection Rule for Estimating the Parameters of 2-D Sinusoids in Colored Noise

2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings, 2006

We consider the problem of jointly estimating the number as well as the parameters of two-dimensional sinusoidal signals, observed in the presence of an additive colored noise field. In this framework we consider the problem of least squares estimation of the parameters of 2-D sinusoidal signals observed in the presence of an additive noise field, when the assumed number of ...


Signal segmentation using maximum a posteriori probability estimator

2013 7th International Conference on Application of Information and Communication Technologies, 2013

The objective of the paper is to present a segmentation method, using maximum a posteriori probability (MAP) estimator, with application in decision making, based on change detection and diagnosis. Some experimental results obtained by Monte-Carlo simulations for signal segmentation using different signal models, including models with changes in the mean, in FIR, AR and ARX model parameters, that make the ...


Inversion of large-support ill-posed linear operators using a piecewise Gaussian MRF

IEEE Transactions on Image Processing, 1998

We propose a method for the reconstruction of signals and images observed partially through a linear operator with a large support (e.g., a Fourier transform on a sparse set). This inverse problem is ill-posed and we resolve it by incorporating the prior information that the reconstructed objects are composed of smooth regions separated by sharp transitions. This feature is modeled ...


Adaptively altering hyper-parameter for improved reconstruction in PET

2003 IEEE Nuclear Science Symposium. Conference Record (IEEE Cat. No.03CH37515), 2003

It is well known that as the iterations of the maximum likelihood algorithm are continued, density estimates become more and more noisy. In situations where some prior knowledge about the object distribution is available, one may utilize such information in the reconstruction procedure for improving the reconstruction. Fixed prior based image reconstruction process produces over- smooth reconstruction due to the ...



Educational Resources on Maximum a posteriori estimation

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IEEE-USA E-Books

  • Maximum a posteriori state estimation: a neural processing algorithm

    A computational algorithm is presented for obtaining the maximum a posteriori estimates of the states of a stochastic dynamical system by programming a neural network. It is well known that for real-time control implementations, especially in such applications as multitarget tracking and vision-guided robots, the computational requirements for solving such state estimation problems attain particular significance, and parallel processing techniques are highly useful. The performance of the algorithm has been investigated by conducting several numerical experiments. It appears to be useful for handling state estimation problems arising in real-world applications.<<ETX>>

  • Model Order Selection Rule for Estimating the Parameters of 2-D Sinusoids in Colored Noise

    We consider the problem of jointly estimating the number as well as the parameters of two-dimensional sinusoidal signals, observed in the presence of an additive colored noise field. In this framework we consider the problem of least squares estimation of the parameters of 2-D sinusoidal signals observed in the presence of an additive noise field, when the assumed number of sinusoids is incorrect. In the case where the number of sinusoidal signals is under-estimated we show the almost sure convergence of the least squares estimates to the parameters of the dominant sinusoids. In the case where the number of sinusoidal signals is over-estimated, the estimated parameter vector obtained by the least squares estimator contains a sub-vector that converges almost surely to the correct parameters of the sinusoids. Based on these results, we prove the strong consistency of a large family of model order selection rules

  • Signal segmentation using maximum a posteriori probability estimator

    The objective of the paper is to present a segmentation method, using maximum a posteriori probability (MAP) estimator, with application in decision making, based on change detection and diagnosis. Some experimental results obtained by Monte-Carlo simulations for signal segmentation using different signal models, including models with changes in the mean, in FIR, AR and ARX model parameters, that make the object of investigation in other papers, are presented to prove the effectiveness of the approach.

  • Inversion of large-support ill-posed linear operators using a piecewise Gaussian MRF

    We propose a method for the reconstruction of signals and images observed partially through a linear operator with a large support (e.g., a Fourier transform on a sparse set). This inverse problem is ill-posed and we resolve it by incorporating the prior information that the reconstructed objects are composed of smooth regions separated by sharp transitions. This feature is modeled by a piecewise Gaussian (PG) Markov random field (MRF), known also as the weak-string in one dimension and the weak-membrane in two dimensions. The reconstruction is defined as the maximum a posteriori estimate. The prerequisite for the use of such a prior is the success of the optimization stage. The posterior energy corresponding to a PG MRF is generally multimodal and its minimization is particularly problematic. In this context, general forms of simulated annealing rapidly become intractable when the observation operator extends over a large support. Global optimization is dealt with by extending the graduated nonconvexity (GNC) algorithm to ill-posed linear inverse problems. GNC has been pioneered by Blake and Zisserman (1987) in the field of image segmentation. The resulting algorithm is mathematically suboptimal but it is seen to be very efficient in practice. We show that the original GNC does not correctly apply to ill-posed problems. Our extension is based on a proper theoretical analysis, which provides further insight into the GNC. The performance of the proposed algorithm is corroborated by a synthetic example in the area of diffraction tomography.

  • Adaptively altering hyper-parameter for improved reconstruction in PET

    It is well known that as the iterations of the maximum likelihood algorithm are continued, density estimates become more and more noisy. In situations where some prior knowledge about the object distribution is available, one may utilize such information in the reconstruction procedure for improving the reconstruction. Fixed prior based image reconstruction process produces over- smooth reconstruction due to the penalizing nature of the potential. As the reconstruction process builds up, image properties like smoothness, frequency content etc., change and hence fixed prior based image reconstruction process may not serve the purpose. A new prior is proposed which is capable of producing improved reconstruction over those obtained by existing fixed prior based Bayesian algorithms. These are termed as dynamic priors, which unlike other priors modify itself recursively according to the noise level in the reconstruction. It is found that inclusion of prior knowledge in the reconstruction algorithm results in local minimums. In the present approach, appropriate prior energy is supplied to the estimate to overcome local minimums by scheduling Gibbs hyperparameter and subsequently the effect of prior is removed recursively as the estimate approaches global minimum.

  • An efficient turbo decoder architecture for IMT2000

    In this paper, we present an efficient architecture of turbo decoder for IMT2000 system. We introduce a base 2 logarithmic maximum a-posteriori algorithm (log/sub 2/MAP) whose architecture is simpler than that of the conventional natural logarithmic MAP algorithm (log/sub e/MAP). With log/sub 2/MAP, we obtain a '2 function' which is simpler than the 'E function' used by log/sub e/MAP. In order to implement the architecture of the 2 function, we use approximated binary logarithmic algorithm (ABLA) which has been usefully adopted in DSP. Using ABLA, we can reduce the RAM size from 1 kbytes to 96 bytes, which can be implemented using combinational logic gates. Also, we design the simple normalization module by making all the branch metrics to have positive values. We introduce reverse interleaver and deinterleaver to calculate forward and reverse state metric simultaneously. Using our architecture, we obtained BER of 9.79/spl times/10/sup -7/ at Eb/No of 2 dB and 5th iterations for constraint length K=4, code rate R=1/2, jumping window of 512 bits and interleaver size of 1144 bits, i.e. data rate of 57.6 kbps.

  • Graduated nonconvexity algorithm for image estimation using compound Gauss Markov field models

    The authors describe the development of a deterministic algorithm for obtaining the global maximum a posteriori probability (MAP) estimate from an image corrupted by additive Gaussian noise. The MAP algorithm requires the probability density function of the original undegraded image and the corrupting noise. It is assumed that the original image is represented by a compound model consisting of a 2-D noncausal Gaussian-Markov random field (GMRF) to represent the homogeneous regions and a line process model to represent the discontinuities. The MAP algorithm is written in terms of the compound GMRF model parameters. The solution to the MAP equations is realized by a deterministic relaxation algorithm that is an extension of the graduated nonconvexity (GNC) algorithm and finds the global MAP estimate in a small number of iterations. As a byproduct, the line process configuration determined by the MAP estimate produces an accurate edge map without any additional cost. Experimental results are given to illustrate the usefulness of the method.<<ETX>>

  • Predictive Density Approach To Parametric Classification

    None

  • Remote brain image segmentation

    Quantitative analysis of brain magnetic resonance (MR) images requires methods of tissue classification. New Web and Internet technologies allow remote MR image classification and visualization through browser functionality. Our application uses a method of segmentation based on the Markov random field (MRF) modeling of maximum a-posteriori (MAP) probability. The result of the segmentation can also be visualised as a VRML (Virtual Reality Modeling Language) world.

  • Packet Combining Over Rayleigh Channels using Signal-to-Noise Ratio Information and Detection by the Maximum A-Posteriori Criterion

    We present and analyze a packet combining strategy for wireless networks with slow Rayleigh fading. The scheme is based on adding the current signal-to- noise ratio (SNR) as an overhead to the packet and packet combining using the maximum a posteriori (MAP) criterion. We consider single and multiple wireless hops, and we perform comparisons against the optimum case, the maximum ratio combiner (MRC). For the single wireless hop, we show that the error probability curve is very close to the optimum. In addition, we study the effect of selection and an alternative based on averaging over the channel statistics that has poorer error performance, but needs less processing and overhead. In multiple wireless hops, some nodes act as relays and the error probability increases. However, combining several branches, each with two hops, allows for diversity order equal to the number of branches. We demonstrate the performance of the proposed strategy by computer simulations. The fusing packet scheme that is presented in this paper is adequate for sensor networks



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