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检索条件"主题词=expectation-maximisation algorithm"

共 495 条记录，以下是411-420 订阅

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Learning Marked Markov Modulated Poisson Processes for Online Predictive Analysis of Attack Scenarios

Learning Marked Markov Modulated Poisson Processes for Onlin...

引用

International Symposium on Software Reliability Engineering (ISSRE)

作者： Laura Carnevali Francesco Santoni Enrico Vicario Department of Information Engineering University of Florence Firenze Italy

ISBN: (纸本)9781728149837

Runtime predictive analysis of quantitative models can support software reliability in various application scenarios. The spread of logging technologies promotes approaches where such models are learned from observed events. We consider a system visiting transient states of a hidden process until reaching a final state and producing observations with stochastic arrival times and types conditioned by visited states, and we abstract it as a marked Markov modulated Poisson Process (MMMPP) with left-to right structure. We present an expectation-Maximization (EM) algorithm that learns the MMMPP parameters from observation sequences acquired in repeated execution of the transient behavior, and we use the model at runtime to infer the current state of the process from actual observed events and to dynamically evaluate the remaining time to the final state. The approach is illustrated using synthetic datasets generated from a stochastic attack tree of the literature enriched with an observation model associating each state with an expected statistics of observation types and arrival times. Accuracy of prediction is evaluated under different variability of hidden states sojourn durations and of the observations arrival process, and compared against previous literature that mainly exploits either the timing or the types of observed events.

关键词： expectation-maximisation algorithm learning (artificial intelligence) Markov processes security of data software reliability expectation-maximisation algorithm Software reliability Data Security Poisson process Markov chain Runtime Markov

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A Hierarchical Bayesian Markovian Model for Motifs in Biopolymer Sequences 02

A Hierarchical Bayesian Markovian Model for Motifs in Biopol...

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Annual Conference on Neural Information Processing Systems

作者： Eric P. Xing Michael I. Jordan Richard M. Karp Stuart Russell Computer Science Division University of California Berkeley Berkeley CA 94720

ISBN: (纸本)0262025507

We propose a dynamic Bayesian model for motifs in biopolymer sequences which captures rich biological prior knowledge and positional dependencies in motif structure in a principled way. Our model posits that the position-specific multinomial parameters for monomer distribution are distributed as a latent Dirichlet-mixture random variable, and the position-specific Dirichlet component is determined by a hidden Markov process. Model parameters can be fit on training motifs using a vari-ational EM algorithm within an empirical Bayesian framework. Varia-tional inference is also used for detecting hidden motifs. Our model improves over previous models that ignore biological priors and positional dependence. It has much higher sensitivity to motifs during detection and a notable ability to distinguish genuine motifs from false recurring patterns.

关键词： Biopolymers Bayesian inference expectation-maximisation algorithm HIGH SENSITIVITY Hierarchical

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REM-Seg: A Robust EM algorithm for Parallel Segmentation and Registration of Point Clouds

REM-Seg: A Robust EM Algorithm for Parallel Segmentation and...

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IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Benjamin Eckart Alonzo Kelly Robotics Institute Carnegie Mellon University Pittsburgh PA USA

ISBN: (纸本)9781467363563

For purposes of real-time 3D sensing, it is important to be able to quickly register together incoming point cloud data. In this paper, we devise a method to quickly and robustly decompose large point clouds into a relatively small number of meaningful surface patches from which we register new data points. The surface patch representation sidesteps the costly problem of matching points to points since incoming data only need to be compared with the patches. The chosen parametrization of the patches (as Gaussians) leads to a smooth data likelihood function with a well-defined gradient. This representation thus forms the basis for a robust and efficient registration algorithm using a parallelized gradient descent implemented on a GPU using CUDA. We use a modified Gaussian Mixture Model (GMM) formulation solved by expectation Maximization (EM) to segment the point cloud and an annealing gradient descent method to find the 6-DOF rigid transformation between the incoming point cloud and the segmented set of surface patches. We test our algorithm, Robust EM Segmentation (REM-Seg), against other GPU-accelerated registration algorithms on simulated and real data and show that our method scales well to large numbers of points, has a wide range of convergence, and is suitably accurate for 3D registration.

关键词： Parallel Segmentation Registration EM Registration expectation-maximisation algorithm Clouds Large Numbers Patches wide-range Robustness Gauss

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Strategies to Improve Direct EM Patlak Reconstructions

Strategies to Improve Direct EM Patlak Reconstructions

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IEEE Nuclear Science Symposium

作者： Jean-Dominique Gallezot Yihuan Lu Kathryn Fontaine Chi Liu Richard E. Carson Department of Radiology and Biomedical Imaging Yale University

ISBN: (纸本)9781538622834

Direct expectation-Maximization (EM) reconstruction of parametric images from PET data has been proposed, to take advantage of the known Poisson character of raw PET data. However, direct reconstruction algorithms are then dependent on the properties of the kinetic model and of the radiotracer used. Moreover, the first direct EM reconstruction algorithm proposed, the Parametric Iterative Reconstruction (PIR) algorithm, based on the Patlak plot, converges slowly when applied to [~(18)F]FDG data. Thus, alternative algorithms have been proposed to accelerate convergence. In this study, we propose additional strategies to improve the PIR algorithm. One strategy is to use a line search procedure to increase the size of steps taken at each iteration (PIR-LS). Another group of strategies is to apply physiological constraints (PIR-PC) on the intercept parameter of the Patlak plot, since this parameter has a small dynamic range and is more stable across subjects and across physiological and disease states. The third strategy is to apply a linear transform to the independent variables of the Patlak model (PIR-LTIV) to obtain new independent variables more favorable for convergence. PIR-LS, PIR-PC and PIR-LTIV were first compared to PIR and the Nested EM algorithm on simulated 2D data. Results showed that all new methods could increase the convergence speed of PIR, with PIR-LTIV having the fastest convergence among the new methods, and similar to that of the Nested EM algorithm. Then, PIR and PIR-LS were compared using a simulated dynamic brain dataset, PIR and PIR-PC were compared on a real lung tumor data set, and PIR-LTIV was tested on simulated brain data, real brain data, and the real lung tumor dataset. These data confirmed that all new methods could increase PIR convergence, with PIR-LTIV being the most promising since convergence speed was visually similar to that of indirect reconstructions, with lower image noise.

关键词： PIR gene new methods expectation-maximisation algorithm Converge image noise Lung Neoplasms line retrieval Reconstruction algorithm Reconstructive Surgical Procedures Independent Variables Dynamic range

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A Scenario-based Approach to Parameter Estimation in State-Space Models having Quantized Output Data

A Scenario-based Approach to Parameter Estimation in State-S...

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2010 49th IEEE Conference on Decision and Control

作者： Damian E. Marelli Boris I. Godoy Graham C. Goodwin ARC Centre of Excellence for Complex Dynamic Systems and Control The University of Newcastle Australia

ISBN: (纸本)9781424477456

In this paper we describe an algorithm for estimating the parameters of a linear, discrete-time system, in state-space form, having quantized measurements. The estimation is carried out using the maximum likelihood criterion. The solution is found using the expectation maximization (EM) algorithm. A technical difficulty in applying this algorithm for this problem is that the a posteriori probability density function, found in the EM algorithm, is non-Gaussian. To deal with this issue, we sequentially approximate it using scenarios, i.e., a weighted sum of impulses which are deterministically computed. Numerical experiments show that the proposed approach leads to a significantly more accurate estimation than the one obtained by ignoring the presence of the quantizer and applying standard estimation methods.

关键词： expectation-maximisation algorithm Parameter estimation Probability density function quantizer weight sums space configuration a posteriori probability

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algorithmic modelling of structural connectivity for process plants

Algorithmic modelling of structural connectivity for process...

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American Control Conference

作者： Temitayo Bankole Debangsu Bhattacharyya Department of Chemical Engineering West Virginia University Morgantown 26506 USA

ISBN: (纸本)9781467386838

In this paper, we propose a new systematic methodology to identify dynamic changes in the connectivity among various equipment items in a process plant. Drawing analogy to neurological systems, the paper develops a framework to obtain dynamic causal model to capture the intrinsic and stimulus-driven connectivity among equipment items. An expectation maximization algorithm following a Bayesian framework is employed to obtain the elements of the connectivity matrices using a maximum likelihood approach. The algorithm is tested on a reactor-separator system. The proposed approach can help to decompose large-scale process plants into strongly-connected and weakly-connected systems. If only strongly-connected equipment items are considered together for control structure selection, this approach can result in a computationally less intensive problem for dynamic controlled variable selection of large-scale process plants.

关键词： Mathematical model Kernel Process control Bayes methods Jacobian matrices Covariance matrices Heuristic algorithms Jacobian matrix Heuristic algorithms Bayes methods Process plants Process control Mathematical Model variance covariance matrix Kernel Covariance matrix expectation-maximisation algorithm maximum likelihood

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Regularized EM algorithms: A Unified Framework and Statistical Guarantees 15

Regularized EM Algorithms: A Unified Framework and Statistic...

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Annual Conference on Neural Information Processing Systems

作者： Xinyang Yi Constantine Caramanis Dept. of Electrical and Computer Engineering The University of Texas at Austin

ISBN: (纸本)9781510825024

Latent models are a fundamental modeling tool in machine learning applications, but they present significant computational and analytical challenges. The popular EM algorithm and its variants, is a much used algorithmic tool; yet our rigorous understanding of its performance is highly incomplete. Recently, work in has demonstrated that for an important class of problems, EM exhibits linear local convergence. In the high-dimensional setting, however, the M-step may not be well defined. We address precisely this setting through a unified treatment using regularization. While regularization for high-dimensional problems is by now well understood, the iterative EM algorithm requires a careful balancing of making progress towards the solution while identifying the right structure (e.g., sparsity or low-rank). In particular, regularizing the M-step using the state-of-the-art high-dimensional prescriptions (e.g., a la) is not guaranteed to provide this balance. Our algorithm and analysis are linked in a way that reveals the balance between optimization and statistical errors. We specialize our general framework to sparse gaussian mixture models, high-dimensional mixed regression, and regression with missing variables, obtaining statistical guarantees for each of these examples.

关键词： expectation-maximisation algorithm Frameworks algorithms Regression (Psychology) variants

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NOISE ROBUST SPEECH DEREVERBERATION WITH KALMAN SMOOTHER

NOISE ROBUST SPEECH DEREVERBERATION WITH KALMAN SMOOTHER

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IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Masahito Togami Yohei Kawaguchi Central Research Laboratory Hitachi Ltd. 1-280 Higashi-koigakubo Kokubunji-shi Tokyo 185-8601 Japan

ISBN: (纸本)9781479903573

A speech dereverberation method is proposed that is robust against background noise. In contrast to conventional methods based on the linear prediction of the given microphone input signal, in which the linear prediction coefficients are not fully optimized when there is background noise, the proposed method optimizes the coefficients by linear prediction of the noiseless reverberant speech signal even when there is background noise. The noiseless reverberant speech signal and the parameters are iteratively updated on the basis of the expectation maximization algorithm. In the expectation step, sufficient statistics of latent variables which include noiseless reverberant speech signal are estimated using the Kalman smoother. Unlike the standard Kalman smoother, which uses a time-invariant covariance matrix as a state-transition covariance matrix, the proposed method utilizes a time-varying covariance matrix, enabling it to meet the time-varying speech characteristics. The parameters are updated so that the Q function is increased in the maximization step. Experimental results show that the proposed method is superior to conventional methods under noisy conditions.

关键词： Voice Signal linear prediction BACKGROUND NOISE Q FUNCTIONS expectation-maximisation algorithm Noise variance covariance matrix Speech Characteristics Speech Covariance matrix

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Visual Tracking Based on 3D Probabilistic Reconstruction

Visual Tracking Based on 3D Probabilistic Reconstruction

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Annual IEEE International Conference on Engineering of Computer Based Systems

作者： Gisele Simas Rodrigo de Bern Lucas Novelo Guilherme Fickel Silvia Botelho Centro de Ciencias Computacionais - C3 Universidade Federal do Rio Grande - FURG

ISBN: (纸本)9781424465378;9780769540054

This paper presents an approach to the 3D visual tracking problem into multi-camera environments. This proposal executes the markerless visual tracking observing the environment through a model based in a volumetric reconstruction technique, called 3D Probabilistic Occupancy Grids, which is still seldom used for this purpose. The target is tracked by the use of expectation-Maximization algorithm with an object representation model constructed with Gaussians blobs representing the object body parts.

关键词： visual tracking expectation-maximisation algorithm Environments Reconstructive Surgical Procedures

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A Signal Processing Technique for Compensating Random Drift of MEMS Gyros

A Signal Processing Technique for Compensating Random Drift ...

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2014中国制导、导航与控制学术会议

作者： Jieyu Liu Qiang Shen Weiwei Qin Department of Automatic Control Xi''an Research Institute of High-techXi''anShannxi Province710025China

ISBN: (纸本)9781479946983

In this paper,we present a prediction and compensation method for Micro-Electro-Mechanical System (MEMS) gyroscope random drift,which is based on relevance vector *** relevance vector machine (RVM) model is established based on the feature of MEMS gyroscope random drift and the parameters are trained by the expectation Maximization (EM) *** phase space reconstruction,the time sequence of random drift is accessed in the *** final experimental results indicate that our proposed methodology can achieve both the least complexity of structure and goodness of fit to data,and also can predict the gyroscope random drift ***,by compensating random drift using the predicting result,the precision of gyroscopes application could be improved well.

关键词： compensation expectation-maximisation algorithm gyroscopes learning (artificial intelligence) microsensors signal processing EM algorithm MEMS gyroscope RVM model compensation method expectation maximization algorithm microelectromechanical systems phase space reconstruction prediction method random drift compensation relevance vector machine signal processing technique Bayes methods Gyroscopes Kernel Micromechanical devices Prediction algorithms Support vector machines Vectors

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