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Multicast-based loss inference with missing data

IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2002年第4期20卷 700-713页

作者： Duffield, NG Horowitz, J Towsley, D Wei, W Friedman, T AT&T Labs Res Florham Pk NJ 07932 USA Univ Massachusetts Dept Math & Stat Amherst MA 01003 USA Univ Massachusetts Dept Comp Sci Amherst MA 01003 USA

Network tomography using multicast probes enables inference of loss characteristics of internal network links from reports of end-to-end loss seen at multicast receivers. In this paper, we develop estimators for internal loss rates when reports are not available on all probes or from all receivers. This problem is motivated by the use of unreliable transport protocols, such as reliable transport protocol, to transmit loss reports to a collector for inference. We use a maximum-likelihood (NIL) approach in which we apply the expectation maximization (EM) algorithm to provide an approximating solution to the the NIL estimator for the incomplete data problem. We present a concrete realization of the algorithm that can be applied to measured data. For classes of models. we establish identifiability of the probe and report loss parameters, and convergence of the EM sequence to the maximum-likelihood estimator (MLE). Numerical results suggest that these properties hold more generally. We derive convergence rates for the EM iterates, and the estimation error of the MLE. Finally, we evaluate the accuracy and convergence rate through extensive simulations.

关键词： end-to-end measurement expectation maximization algorithm maximum-likelihood estimation missing data multicast network tomography reliable transport control protocol reliable transport protocol

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Probabilistic typology of management decision making in private forest properties

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FOREST POLICY AND ECONOMICS 2013年 27卷 34-43页

作者： Ficko, Andrej Boncina, Andrej Univ Ljubljana Biotech Fac Dept Forestry & Renewable Forest Resources Ljubljana 1000 Slovenia

We conducted a quantitative study of private forest owner management behavior based on face-to-face interviews with 380 randomly selected private forest owners in Slovenia. Forest owners were asked to rate the relevance of nineteen factors representing information related to the social, ecological, and economic aspects of decision making based on a five-point Likert scale. This information was consolidated into major categories with Principal Component Analysis. expectation maximization (EM) clustering was used to build a probabilistic private forest owner derision making typology. Six major categories of information determined 64% of the variability in decision making: non-wood goods and services, forest economics, property administration, optimization of wood production, forest protection, and minimum cutting restrictions. EM clustering revealed two decision making types differing in their attitude towards the total economic value of forests: Materialists, whose decisions are mainly related to the extractive value of forests and Non-materialists, who manage for non-extractive vale. Full-time farmers, owners living within 2 km of their holdings, and owners who permanently cooperated with the public forest service were much more likely to be Materialists. The uncertainty in private forest owner typology building and the applicability of probabilistic models of private forest owners to end-users is discussed. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Private forest owners Value of information Management decision Management behavior expectation maximization algorithm Slovenia

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A generalized BCJR algorithm and its use in iterative blind channel identification

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IEEE SIGNAL PROCESSING LETTERS 2007年第10期14卷 661-664页

作者： Gunther, Jake Keller, David Moon, Todd Utah State Univ Logan UT 84322 USA

Recently, the well-known Bahl-Cocke-Jelinek-Raviv (BCJR) algorithm was generalized to compute joint posterior probabilities of arbitrary sets of symbols given noisy observations of those symbols at the output of an intersymbol interference (ISI) channel. This letter explores using pair-wise joint posterior probabilities produced by generalized BCJR together with expectation maximization for blind identification of the ISI channel impulse response and noise variance. Simulations indicate that the blind algorithm accurately estimates the channel response and noise variance and yields bit error rates comparable to a channel-informed BCJR equalizer.

关键词： Bahl-Cocke-Jelinek-Raviv (BUR) algorithm blind channel identification expectation maximization algorithm

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Exact filters for certain moments and stochastic integrals of the state of systems with Benes nonlinearity

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 1999年第10期44卷 1929-1933页

作者： Elliott, RJ Krishnamurthy, V Poor, HV Univ Alberta Dept Math Sci Edmonton AB T6G 2G1 Canada Univ Melbourne Dept Elect & Elect Engn Parkville Vic 3052 Australia Princeton Univ Dept Elect Engn Princeton NJ 08544 USA

Finite-dimensional filters for integrals and stochastic integrals of moments of the state for continuous-time nonlinear systems with Benes nonlinearity are derived. These new filters can be used with the expectation m... 详细信息

关键词： Benes filter expectation maximization algorithm finite-dimensional filters parameter estimation stochastic systems

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Implementing wavelet transform/mixture of experts network for analysis of electrocardiogram beats

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EXPERT SYSTEMS 2008年第2期25卷 150-162页

作者： Ubeyli, Elif Derya TOBB Ekonomi & Teknol Univ Fac Engn Dept Elect & Elect Engn TR-06530 Ankara Turkey

Mixture of experts (ME) is a modular neural network architecture for supervised learning. This paper illustrates the use of the ME network structure to guide model selection for classification of electrocardiogram (ECG) beats. The expectation maximization algorithm is used for training the ME so that the learning process is decoupled in a manner that fits well with the modular structure. The ECG signals were decomposed into time-frequency representations using discrete wavelet transforms and statistical features were calculated to depict their distribution. The ME network structure was implemented for ECG beats classification using the statistical features as inputs. To improve classification accuracy, the outputs of expert networks were combined by a gating network simultaneously trained in order to stochastically select the expert that is performing the best at solving the problem. Five types of ECG beats (normal beat, congestive heart failure beat, ventricular tachyarrhythmia beat, atrial fibrillation beat, partial epilepsy beat) obtained from the Physiobank database were classified with an accuracy of 96.89% by the ME network structure. The ME network structure achieved accuracy rates which were higher than those of the stand-alone neural network models.

关键词： mixture of experts expectation maximization algorithm classification accuracy discrete wavelet transform ECG beats classification

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A Bayesian EM algorithm for optimal tracking of a maneuvering target in clutter

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SIGNAL PROCESSING 2002年第3期82卷 473-490页

作者： Logothetis, A Krishnamurthy, V Holst, J Ericsson Telecom AB S-12625 Stockholm Sweden

The difficulty in tracking a maneuvering target in the presence of false measurements arises from the uncertain origin of the measurements (as a result of the observation detection process) and the uncertainty in the maneuvering command driving the state of the target. Conditional mean estimates of the target state require a computational cost which is exponential with the number of observations and the levels of the maneuver command. In this paper, we propose an alternative optimal state estimation algorithm. Unlike the conditional mean estimator, which require computational cost exponential in the data length, the proposed iterative algorithm is linear in the data length (per iteration). The proposed iterative off-line algorithm optimally combines a hidden Markov model and a Kalman smoother-the optimality is demonstrated via the expectation maximization algorithm-to yield the maximum a posteriori trajectory estimate of the target state. The algorithm proposed in this paper, uses probabilistic multi-hypothesis (PMHT) techniques for tracking a single maneuvering target in clutter. The extension of our algorithm to multiple maneuvering target tracking is straightforward and details are omitted. Previous applications of the PMHT technique (IEEE Trans. Automat. Control, submitted) have addressed the problem of tracking multiple non-maneuvering targets. These techniques are extended to address the problem of optimal tracking of a maneuvering target in a cluttered environment. (C) 2002 Elsevier Science B.V. All rights reserved.

关键词： expectation maximization algorithm maneuvering target tracking PMHT clutter

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Self-Optimal Clustering Technique Using Optimized Threshold Function

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IEEE SYSTEMS JOURNAL 2014年第4期8卷 1213-1226页

作者： Verma, Nishchal K. Roy, Abhishek Indian Inst Technol Kanpur 208016 Uttar Pradesh India Natl Inst Technol Karnataka Surathkal Mangalore 575025 India

This paper presents a self-optimal clustering (SOC) technique which is an advanced version of improved mountain clustering (IMC) technique. The proposed clustering technique is equipped with major changes and modifications in its previous versions of algorithm. SOC is compared with some of the widely used clustering techniques such as K-means, fuzzy C-means, expectation and maximization, and K-medoid. Also, the comparison of the proposed technique is shown with IMC and its last updated version. The quantitative and qualitative performances of all these well-known clustering techniques are presented and compared with the aid of case studies and examples on various benchmarked validation indices. SOC has been evaluated via cluster compactness within itself and separation with other clusters. The optimizing factor in the threshold function is computed via interpolation and found to be effective in forming better quality clusters as verified by visual assessment and various standard validation indices like the global silhouette index, partition index, separation index, and Dunn index.

关键词： expectation maximization algorithm fuzzy cardinality improved mountain clustering (IMC) interpolation polynomial

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Speech Enhancement Based on Approximate Message Passing

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China Communications 2020年第8期17卷 187-198页

作者： Chao Li Ting Jiang Sheng Wu School of Information and Communication Engineering Beijing University of Posts and TelecommunicationsBeijing 100876China

To overcome the limitations of conventional speech enhancement methods, such as inaccurate voice activity detector(VAD) and noise estimation, a novel speech enhancement algorithm based on the approximate message passing(AMP) is adopted. AMP exploits the difference between speech and noise sparsity to remove or mute the noise from the corrupted speech. The AMP algorithm is adopted to reconstruct the clean speech efficiently for speech enhancement. More specifically, the prior probability distribution of speech sparsity coefficient is characterized by Gaussian-model, and the hyper-parameters of the prior model are excellently learned by expectation maximization(EM) algorithm. We utilize the k-nearest neighbor(k-NN) algorithm to learn the sparsity with the fact that the speech coefficients between adjacent frames are correlated. In addition, computational simulations are used to validate the proposed algorithm, which achieves better speech enhancement performance than other four baseline methods-Wiener filtering, subspace pursuit(SP), distributed sparsity adaptive matching pursuit(DSAMP), and expectation-maximization Gaussian-model approximate message passing(EM-GAMP) under different compression ratios and a wide range of signal to noise ratios(SNRs).

关键词： speech enhancement approximate message passing Gaussian model expectation maximization algorithm

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New finite-dimensional filters for parameter estimation of discrete-time linear Gaussian models

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 1999年第5期44卷 938-951页

作者： Elliott, RJ Krishnamurthy, V Univ Alberta Dept Math Sci Edmonton AB T6G 2G1 Canada Univ Melbourne Dept Elect & Elect Engn Parkville Vic 3052 Australia

In this paper the authors derive a new class of finite-dimensional recursive filters for linear dynamical systems. The Kalman filter is a special case of their general filter. Apart from being of mathematical interest, these new finite-dimensional filters can be used with the expectation maximization (EM) algorithm to yield maximum likelihood estimates of the parameters of a linear dynamical system. Important advantages of their filter-based EM algorithm compared with the standard smoother-based EM algorithm include: 1) substantially reduced memory requirements and 2) ease of parallel implementation on a multiprocessor system. The algorithm has applications in multisensor signal enhancement of speech signals and also econometric modeling.

关键词： expectation maximization algorithm finite-dimensional filters Kalman filter maximum likelihood parameter estimation

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Importance Weighted expectation-maximization for Protein Sequence Design

arXiv

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arXiv 2023年

作者： Song, Zhenqiao Li, Lei Department of Computer Science University of California Santa BarbaraCA United States

Designing protein sequences with desired biological function is crucial in biology and chemistry. Recent machine learning methods use a surrogate sequence-function model to replace the expensive wet-lab validation. How can we efficiently generate diverse and novel protein sequences with high fitness? In this paper, we propose IsEM-Pro, an approach to generate protein sequences towards a given fitness criterion. At its core, IsEM-Pro is a latent generative model, augmented by combinatorial structure features from a separately learned Markov random fields (MRFs). We develop an Monte Carlo expectation-maximization method (MCEM) to learn the model. During inference, sampling from its latent space enhances diversity while its MRFs features guide the exploration in high fitness regions. Experiments on eight protein sequence design tasks show that our IsEM-Pro outperforms the previous best methods by at least 55% on average fitness score and generates more diverse and novel protein sequences. The code is available at https://***/JocelynSong/*** Copyright © 2023, The Authors. All rights reserved.

关键词： expectation maximization algorithm

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