Introduces a series of articles on the developments in iterative image reconstruction for positron-emission tomography (PET) and single proton emission computer tomography (SPECT).
Introduces a series of articles on the developments in iterative image reconstruction for positron-emission tomography (PET) and single proton emission computer tomography (SPECT).
A statistical model for the object and the complete image formation process in cryo electron microscopy of viruses is presented. Using this model, maximum likelihood reconstructions of the 3D structure of viruses are ...
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ISBN:
(纸本)0819437689
A statistical model for the object and the complete image formation process in cryo electron microscopy of viruses is presented. Using this model, maximum likelihood reconstructions of the 3D structure of viruses are computed using the expectationmaximizationalgorithm and an example based on Cowpea mosaic virus is provided.
A new method for the representation, recognition, and interpretation of parameterized gesture is presented. By parameterized gesture we mean gestures that exhibit a systematic spatial variation;one example is a point ...
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A new method for the representation, recognition, and interpretation of parameterized gesture is presented. By parameterized gesture we mean gestures that exhibit a systematic spatial variation;one example is a point gesture where the relevant parameter is the two-dimensional direction. Our approach is to extend the standard hidden Markov model method of gesture recognition by including a global parametric variation in the output probabilities of the HMM states, Using a linear model of dependence, we formulate an expectation-maximization (EM) method for training the parametric HMM. During testing;a similar EM algorithm simultaneously maximizes the output likelihood of the PHMM for the given sequence and estimates the quantifying parameters. Using visually derived and directly measured three-dimensional hand position measurements as input, we present results that demonstrate the recognition superiority of the PHMM over standard HMM techniques, as well as greater robustness in parameter estimation with respect to noise in the input features. Last, we extend the PHMM to handle arbitrary smooth (nonlinear) dependencies. The nonlinear formulation requires the use of a generalized expectation-maximization (GEM) algorithm for both training and the simultaneous recognition of the gesture and estimation of the value of the parameter. We present results on a pointing gesture, where the nonlinear approach permits the natural spherical coordinate parameterization of pointing direction.
Meta-analysis of clinical trials with heterogeneous results provides an opportunity to learn a great deal about variations in treatment effectiveness. Rather than computing a single summary estimate of-a series of tri...
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Meta-analysis of clinical trials with heterogeneous results provides an opportunity to learn a great deal about variations in treatment effectiveness. Rather than computing a single summary estimate of-a series of trials it may be more informative to explore the effect that different study characteristics may make on treatment efficacy Regression analysis offers a tool for these analyses. This paper outlines and applies hierarchical Bayesian models for this purpose, presenting two examples of meta-regression using summary data, in one of which results are compared with those from analysis of complete individual patient data. When covariates are not readily available, the event rate in the control group can become a surrogate covariate. An empirical study of 115 meta-analyses shows that this control rate is significantly correlated with the odds ratio about 15% of the time. This suggests that investigators should search for the causes of heterogeneity related to patient characteristics and treatment protocols to determine when treatment is most beneficial and that they should plan to study this heterogeneity in clinical trials.
Atmospheric deposition can be a significant source of phosphorus to South Florida's aquatic system. The weekly total phosphorus (TP) concentrations in rainfall have been measured routinely in the region since 1974...
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Atmospheric deposition can be a significant source of phosphorus to South Florida's aquatic system. The weekly total phosphorus (TP) concentrations in rainfall have been measured routinely in the region since 1974, but the historical data set has significant gaps due to instrumental failures and sample contamination. This study attempts to develop a statistical model of rainfall-borne TP concentration to estimate missing data. The model is based on a multivariate stochastic time-series theory. The model parameters and noise covariances were calibrated using the expectation-maximization algorithm which is known to be efficient for data sets with many gaps. Model verification demonstrates that the calibrated model provides unbiased data estimates while preserving the statistics of the raw data. The data with gaps filled in are useful for computing the weekly TP loads. (C) 1999 Elsevier Science B.V. All rights reserved.
A modular and flexible approach to adaptive Kalman filtering has recently been introduced using the framework of a mixture-of-experts regulated by a gating network. Each expert is a Kalman biter modeled with a differe...
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A modular and flexible approach to adaptive Kalman filtering has recently been introduced using the framework of a mixture-of-experts regulated by a gating network. Each expert is a Kalman biter modeled with a different realization of the unknown system parameters. The unknown or uncertain parameters can include elements of the state transition matrix, observation mapping matrix, process noise covariance matrix, and measurement noise covariance matrix. The gating network performs on-line adaptation of the weights given to individual filters based on performance. The mixture-of-experts approach Is extended here to a hierarchical architecture which involves multiple levels of gating. The proposed architecture provides a multilevel hypothesis testing capability, The utility of the hierarchical architecture is illustrated via the problem of interplanetary navigation (Mars Pathfinder) using simulated radiometric data, It serves as a useful tool for assisting navigation teams in the process of selecting the parameters of the navigational biter over various operating regimes. it is shown that the scheme has the capability of detecting changes in the system parameters and switching filters appropriately for optimal performance. Furthermore, the expectation-maximization (EM) algorithm is shown to be applicable in the proposed framework.
For speech recognition based on hidden Markov modeling, parameter-tying, which consists in constraining some of the parameters of the model to share the same value, has emerged as a standard practice. In this paper, a...
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For speech recognition based on hidden Markov modeling, parameter-tying, which consists in constraining some of the parameters of the model to share the same value, has emerged as a standard practice. In this paper, an original algorithm is proposed that makes it possible to jointly estimate both the shared model parameters and the tying characteristics;using the maximum likelihood criterion, The proposed algorithm is based on a recently introduced extension of the classic expectation-maximization (EM) framework. The convergence properties of this class of algorithms are analyzed in detail. The method is evaluated on an isolated word recognition task using hidden Markov models (HMM's) with Gaussian observation densities and tying at the state level. Finally, the extension of this method to the case of mixture observation densities with tying at the mixture component level is discussed.
Dimensionality reduction is an important part of the pattern recognition process. It would be very useful to have a recursive form for dimensionality reduction that is suitable for implementation on massive data sets ...
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Dimensionality reduction is an important part of the pattern recognition process. It would be very useful to have a recursive form for dimensionality reduction that is suitable for implementation on massive data sets and real-time automatic pattern recognition systems. It would also be beneficial to have a version where the dimensionality reduction can be updated based on new partially identified data that are obtained in real systems. Versions of Fisher's Linear Discriminant for dimensionality reduction that address these problems are derived in this article. (C) 1998 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
In the "pick any/n" method, subjects are asked to choose any number of items from a list of n items according to some criterion. This kind of data can be analyzed as a special case of either multiple-choice ...
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In the "pick any/n" method, subjects are asked to choose any number of items from a list of n items according to some criterion. This kind of data can be analyzed as a special case of either multiple-choice data or successive categories data where the number of response categories is limited to two. An item response model was proposed for the latter case, which is a combination of an unfolding model and a choice model. The marginal maximum-likelihood estimation method was developed for parameter estimation to avoid incidental parameters, and an expectation-maximization algorithm used for numerical optimization. Two examples of analysis are given to illustrate the proposed method, which we call MAXSC.
The development of a new method for testing the association of genetic markers with disease is presented. This approach is applicable when sampling nuclear families with one or more affected siblings and when neither,...
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