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Topic: Maximum a posteriori

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	Maximum a posteriori - Wikipedia, the free encyclopedia
		In statistics, the method of maximum a posteriori (MAP, or posterior mode) estimation can be used to obtain a point estimate of an unobserved quantity on the basis of empirical data.
		While MAP estimation shares the use of a prior distribution with Bayesian statistics, it is not generally seen as a Bayesian method.
		This is because MAP estimates are point estimates, whereas Bayesian methods are characterized by the use of distributions to summarize data and draw inferences.
	en.wikipedia.org /wiki/Maximum_a_posteriori (440 words)

	Research on Speech Recognition and Understanding (Site not responding. Last check: 2007-11-04)
		MAP adaptation combines under a well defined mathematical formulation the information provided by the adaptation data with some prior knowledge about the model parameters described by a prior distribution.
		MAP adaptation of HMM parameters performs quite poorly for small amount of adaptation data since only a small fraction of the model parameters are adapted.
		The Structural Maximum A Posteriori Linear Regression, or SMAPLR [10,11], addresses these three issues by structuring the prior information about the transformation matrices in a tree, similar in spirit to the SMAP formulation.
	bell-labs.com /org/1133/Research/SpeechRecognition/sv-adaptation.html (1062 words)

	Parameter Adaptation and Compensation in Designing Maximum A Posteriori Decision Rules for Automatic Speech Recognition ...
		Recent advances in automatic speech recognition are mainly accomplished by designing a plug-in maximum a posteriori decision rule such that the forms of the acoustic and language model distributions are specified and the parameters of the assumed distributions are estimated from a collection of speech and language training examples.
		Maximum likelihood point estimation of decision parameters is by far the most prevailing training method.
		Maximum a posteriori point estimation is then developed.
	www.ima.umn.edu /multimedia/abstract/lee1.html (177 words)

	Maximum a posteriori Deconvolution of Ultrasonic Data with Applications in Nondestructive Testing (Site not responding. Last check: 2007-11-04)
		The deconvolution problem is formulated as estimation of a reflection sequence which is the impulse characteristic of the inspected object and the estimation is performed using either maximum a posteriori (MAP) or linear minimum mean square error (MMSE) estimators.
		Using the MAP estimator three different material types were treated, with varying amount of sparsity in the reflection sequences.
		The Bernoulli--Gaussian distribution is used for sparse data obtained from layered structures and a genetic algorithm approach is proposed for optimizing the corresponding MAP criterion.
	www.eurasip.org /content/phdstuff/phd_abstracts/Olofsson-Tomas.htm (324 words)

	A Pharmacometrics Resource
		A maximum likelihood approach finds that estimate of a parameter, which maximizes the probability of observing the data given a specific model for the data.
		For e.g.: In a maximum likelihood approach, it is that value of the parameter which maximizes the objective function value.
		The prior distribution of the parameters across a population and the actual data of the individual are used to obtain the posterior probability of individual parameter estimates.These estimates are called maximum a posteriori or posthoc estimates.
	www.accp1.org /pharmacometrics/theory.htm (1885 words)

	Maximum a Posteriori Decoding of Turbo Codes > Introduction (Site not responding. Last check: 2007-11-04)
		The MAP decoding algorithm has some similarities to the Viterbi decoding algorithm; the major difference is that the Viterbi algorithm is not equipped to output soft-decision bits that turbo decoding requires.
		The process of turbo-code decoding starts with the formation of a posteriori probabilities (APPs) for each data bit, which is followed by choosing the data-bit value that corresponds to the maximum a posteriori (MAP) probability for that data bit.
		The metrics needed for the implementation of a MAP decoder are presented here, along with an example to illustrate how these metrics are used.
	www.phptr.com /articles/article.asp?p=26038 (274 words)

	Satellite Group: Retrieval Algorithm
		HIMAP, an iterative maximum a posteriori approach using Jacobians, a priori and a priori covariances.
		The Heidelberg iterative maximum a posteriori algorithm is similar to the nonlinear DOAS approach.
		The algorithm maximises the a posteriori probability of the state vector (maximum of the a posteriori probability density function):
	satellite.iup.uni-heidelberg.de /index.php?id=48&type=98 (282 words)

	Proc. SPIE (1990) - Abstract (Site not responding. Last check: 2007-11-04)
		We report on the behavior of the linear maximum a posteriori (MAP) tomographic reconstruction technique as a function of the assumed rms noise in the measurements, which specifies the degree of confidence in the measurement data.
		The unconstrained MAP reconstructions are evaluated on the basis of the performance of two related tasks: object detection and amplitude estimation.
		However, the amplitudes of the discs estimated form the MAP reconstructions increasingly deviate form the actual values as the rms noise increases.
	public.lanl.gov /kmh/publications/medim90.abs.html (130 words)

	CS345, Machine Learning: Notes on Probability and Maximum Likelihood
		In the preceding example, the MAP classifier predicts that a patient with positive lab test results is healthy.
		For example, the maximum likelihood classifier labels a patient with a positive lab result as sick (even though he/she is really more likely to be healthy, as MAP analysis shows).
		Let’s choose h to be the maximum likelihood hypothesis.
	www.cs.bc.edu /~alvarez/ML/ProbabilityNotes.htm (1919 words)

	Research on Speech Recognition and Understanding
		Maximum a posteriori estimation for multivariate gaussian mixture observations of Markov chains.
		Maximum a posteriori linear regression for hidden Markov model adaptation.
		Maximum likelihood linear regression for speaker adaptation of the parameters of continuous density hidden Markov models.
	www.bell-labs.com /org/1133/Research/SpeechRecognition/publications.html (592 words)

	Theses from Uppsala University : MARC 21 81 - Maximum a posteriori deconvulution of ultrasonic data with applications ...
		Theses from Uppsala University : MARC 21 81 - Maximum a posteriori deconvulution of ultrasonic data with applications in nondestructive testing
		$a Maximum a posteriori deconvulution of ultrasonic data with applications in nondestructive testing : $b Multiple transducer and robustness is / $c Tomas Olofsson
		$a Maximum a posteriori deconvulution of ultrasonic data with applications in nondestructive testing, Multiple transducer and robustness is, 2000: $b t.p.
	publications.uu.se /theses/marc21.xsql?dbid=81 (192 words)

	Course Reference
		Using a simple family of models parameterized by p (the probability of heads on a single toss) derive the Maximum Likelihood estimate of p.
		Using a Beta prior on p, with parameters a and b, derive the Maximum A Posteriori Estimate of p.
		Write and run a computer program to estimate the area between a normal density function and the x-axis, from minus one to plus one standard deviation from the mean, by simulating a sequence of Bernoulli trials.
	www.igb.uci.edu /~pfbaldi/hmwk67.htm (359 words)

	Maximum likelihood detection
		This is called the maximum a posteriori decision rule.
		is said to be selected according to the maximum likelihood criterion.
		We shall assume throughout the text a maximum likelihood criterion.
	www.engineering.usu.edu /classes/ece/7670/lecture2/node2.html (143 words)

	Background
		The calculation of posterior marginals for a queried variable can be done by a technique like variable elimination [Zhang and Poole1996] or bucket elimination [Dechter1996], or a more sophisticated clustering method such as peeling [Cannings and Thompson1981] or graph triangulation [Jensen1996].
		Another standard problem is the determination of maximum a posteriori hyposthesis (called MAP) [Dechter1996].
		With this notation, the objective of the MAP algorithm is to obtain:
	www.cs.cmu.edu /~fgcozman/Research/QuasiBayesian/FiniteConvex/node3.html (290 words)

	eLibrary Manuscript Preview Page
		The maximum a posteriori estimates can be obtained by minimizing the resulting objective function.
		It is noted that both the maximum a posteriori estimates and the realizations of the model have practical applications.
		In his work, Oliver presented a procedure to generate the maximum a posteriori estimates and realizations for one-dimensional porosity, and one- and two-dimensional permeability distributions by incorporating the a priori information, hard data, and pressure transient data.
	www.spe.org /elibinfo/eJournal_Papers/spe/2000/ESJ/03/00060224/00060224.htm (843 words)

	Robert (Site not responding. Last check: 2007-11-04)
		An iterative stochastic algorithm to perform maximum a posteriori parameter estimation of hidden Markov models is proposed.
		It makes the most of the statistical model by introducing an artificial probability model based on an increasing number of the unobserved Markov chain at each iteration.
		Some key words: Bayesian estimation; Data augmentation; Hidden Markov models; Maximum a posteriori; Simulated annealing.
	www.bath.ac.uk /~massch/Seminars/Formal/1999-2000/Robert.htm (103 words)

	Fast Robust Inverse Transform SAT and Multi-stage Adaptation
		In addition, we describe a multi-stage approach to Maximum Likelihood Linear Regression (MLLR) unsupervised adaptation and we show that is more effective than a single stage regular MMLR adaptation.
		We therefore estimate priors for MAP adaptation from the training data as follows: at the last iteration of ITSAT, after we inverse-transform the means of each speaker model, we compute the variance between the speaker means for each Gaussian k.
		J. Gauvain, and C. Lee, ``Maximum a Posteriori Estimation for Multivariate Gaussian Mixture Observations of Markov Chains'', IEEE Trans.
	www.nist.gov /speech/publications/darpa98/html/am20/am20.htm (3148 words)

	David S. Lalush
		Lalush, D. S., and B. Tsui, "Attenuation and detector response compensations used with Gibbs prior distributions for maximum a posteriori reconstruction in SPECT," IEEE Transactions on Nuclear Science, vol.
		Lalush, D. S., and B. Tsui, "A generalized Gibbs prior for maximum a posteriori reconstruction in SPECT," Physics in Medicine and Biology, vol.
		Lalush, D. S., and B. Tsui, "Optimization of Gibbs priors based on object size and contrast for maximum a posteriori SPECT reconstruction," Conference Record of the 1992 IEEE Nuclear Science Symposium, Orlando, Florida, pp.
	www.bme.unc.edu /~lalush/homepage.html (783 words)

	The JavaBayes inference algorithm
		Maximization of probabilities and expectations are respectively called the maximum a posteriori hypothesis problem (called MAP) and the maximum expected utility problem (called MEU) MAP and MEU problems seek to obtain:
		A particular case of the MAP problem which has received great attention in the literature is the most probable explanation problem (called MPE), where all variables are decision variables.
		If the objective is to solve a MAP or MEU problem, then the decision variables must come last.
	www.cs.cmu.edu /~fgcozman/Research/JavaBayes/Home/node5.html (878 words)

	i.smap (Site not responding. Last check: 2007-11-04)
		The first mode is the sequential maximum a posteriori (SMAP) mode [1,2].
		The second mode is the more conventional maximum likelihood (ML) classification which classifies each pixel separately, but requires somewhat less computation.
		This new raster map layer will contain categories that can be related to landcover categories on the ground.
	grass.baylor.edu /grass60/manuals/html60_user/i.smap.html (696 words)

	Maximum A Posteriori Pitch Tracking - Droppo, Acero (ResearchIndex) (Site not responding. Last check: 2007-11-04)
		Abstract: A Maximum a posteriori framework for computing pitch tracks as well as voicing decisions is presented.
		The proposed algorithm consists of creating a time-pitch energy distribution based on predictable energy that improves on the normalized cross-correlation.
		Droppo and A. Acero, "Maximum a posteriori pitch tracking," in Proc.
	citeseer.ist.psu.edu /382292.html (490 words)

	High data rate maximum a posteriori decoder for segmented trellis code words (US6192501)
		The segmented MAP decoder operates on code word segments as if they were individual code words and takes advantage of knowing the state of the encoder at specified times to reduce decoding latency and required memory.
		In a turbo coding system, for example, coding gain is maintained by interleaving the information bits across the segments of a component code word.
		Method for a maximum likelihood decoding of a convolutional code with decision weighting, and corresponding decoder
	www.delphion.com /details?pn10=US06192501 (436 words)

	A maximum a posteriori method for plasma tomography
		A maximum a posteriori (MAP) algorithm is a promising statistical approach to this problem.
		In this paper, we study the MAP algorithm which yields the most probable image estimate from limited and noisy data.
		An improvement in reconstruction image quality over conventional tomographic methods, such as the algebraic reconstruction technique and maximum entropy algorithm, is illustrated by several numerical examples.
	stacks.iop.org /0963-0252/13/531 (258 words)

	Person Authentication using Brainwaves (EEG) and Maximum A Posteriori Model Adaptation (Site not responding. Last check: 2007-11-04)
		In this paper, we investigate the use of brain activity for person authentication.
		Person authentication aims to accept or to reject a person claiming an identity, i.e comparing a biometric data to one template, while the goal of person identification is to match the biometric data against all the records in a database.
		We propose the use of a statistical framework based on Gaussian Mixture Models and Maximum A Posteriori model adaptation, successfully applied to speaker and face authentication, which can deal with only one training session.
	www.idiap.ch /publications/marcel-idiap-rr-05-81.bib.abs.html (229 words)

	MaxEnt 1994 Abstract (Site not responding. Last check: 2007-11-04)
		Bayesian methodology provides the means to combine prior knowledge about competing models of reality and available data to draw inferences about the validity of those models.
		This analogy leads to the interpretation of gradient of this potential as a force that acts on the model.
		As model parameters are perturbed from their maximum a posteriori (MAP) values, the strength of the restoring force that drives them back to the MAP solution is directly related to the uncertainty in those parameter estimates.
	public.lanl.gov /kmh/publications/maxent94.abs.html (164 words)

	Maximum A Posteriori Probability Laser Radar Range Profiling (Site not responding. Last check: 2007-11-04)
		Maximum A Posteriori Probability Laser Radar Range Profiling
		We have continued work on MAP laser range profiling using the EM algorithm to deal with speckle-induced range anomalies.
		We have developed a new algorithm that results in a substantial reduction in computational complexity, and we have also initiated the incorporation of multiresolution prior models into the framework, allowing considerable flexibility in incorporating prior information and significant computational advantages.
	www.yorku.ca /tutor/node6.html (75 words)

	IND RWTH Aachen - Veröffentlichungen (Site not responding. Last check: 2007-11-04)
		Noise Reduction by Maximum a Posteriori Spectral Amplitude Estimation with Supergaussian Speech Modeling
		This contribution presents a spectral amplitude estimator for acoustical background noise suppression based on maximum a posteriori estimation and supergaussian statistical modeling of the speech DFT coefficients.
		Based on the approximation, a computationally efficient maximum a posteriori speech estimator is derived, which outperforms the Ephraim-Malah algorithm in a single channel noise reduction framework.
	www.ind.rwth-aachen.de /publications/tl_pv_iwaenc2003.html (112 words)

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