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Multiple model PMHT and its application to the second benchmark radar tracking problem

IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2004年第4期40卷 1337-1350页

作者： Ruan, YH Willett, P Univ Connecticut Storrs CT 06269 USA

The probabilistic multiple hypothesis tracker (PMHT) uses the expectation-maximization (EM) algorithm to solve the measurement-origin uncertainty problem. Here we explore some of its variants for maneuvering targets and in particular discuss the multiple model PMHT. We apply this PMHT to the six "typical" tracking scenarios given in the second benchmark problem from Blair et al. [6]. The manner in which the PMHT is used to track the targets and to manage radar allocation is discussed, and the results compared with those of the interacting multiple model probabilistic data association filter (IMM/PDAF) and IMM/MHT (multiple hypothesis tracker). The PMHT works well: its performance lies between those of the IMM/PDAF and IMM/MHT both in terms of tracking performance and computational load.

关键词： probability radar tracking target tracking tracking filters IMM/MHT IMM/PDAF benchmark radar tracking expectation-maximization algorithm interacting multiple model probabilistic data association filter measurement-origin uncertainty problem multiple mode Benchmark testing expectation-maximisation algorithm radar target tracking Target tracking tracking filters

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A Bayesian model averaging method for software reliability modeling and assessment

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QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL 2023年第3期39卷 958-970页

作者： Li, Zhaojun Steven Jia, Shun Yu, Qiumin Western New England Univ Springfield MA USA Shandong Univ Sci & Technol Qingdao Peoples R China Univ Elect Sci & Technol China Chengdu Peoples R China Western New England Univ Springfield MA 01119 USA

The software reliability modeling is of great significance in improving software quality and managing the software development process. However, the existing methods are not able to accurately model software reliability improvement behavior because existing single model methods rely on restrictive assumptions and combination models cannot well deal with model uncertainties. In this article, we propose a Bayesian model averaging (BMA) method to model software reliability. First, the existing reliability modeling methods are selected as the candidate models, and the Bayesian theory is used to obtain the posterior probabilities of each reliability model. Then, the posterior probabilities are used as weights to average the candidate models. Both Markov Chain Monte Carlo (MCMC) algorithm and the expectation-maximization (EM) algorithm are used to evaluate a candidate model's posterior probability and for comparison purpose. The results show that the BMA method has superior performance in software reliability modeling, and the MCMC algorithm performs better than EM algorithm when they are used to estimate the parameters of BMA method.

关键词： Bayesian model averaging expectation-maximization algorithm Markov Chain Monte Carlo simulation software reliability model

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Ab initio reconstruction and experimental design for cryo electron microscopy

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IEEE TRANSACTIONS ON INFORMATION THEORY 2000年第5期46卷 1714-1729页

作者： Doerschuk, PC Johnson, JE Purdue Univ Sch Elect & Comp Engn W Lafayette IN 47907 USA Scripps Res Inst Dept Mol Biol La Jolla CA 92037 USA

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 three-dimensional (3-D) structure of viruses are computed using the expectation max-imitation algorithm, and alternative experimental designs are evaluated based on Cramer-Rao bounds. Numerical examples of the reconstructions and experimental design evaluations are provided based on Cowpea mosaic virus.

关键词： Cramer-Rao bounds cryo electron microscopy expectation-maximization algorithm statistical inverse problems statistical reconstruction problems viruses

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Model Parameter Estimation and Residual Life Prediction for a Partially Observable Failing System

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NAVAL RESEARCH LOGISTICS 2015年第3期62卷 190-205页

作者： Khaleghei, Akram Makis, Viliam Univ Toronto Dept Mech & Ind Engn Toronto ON M5S 3G8 Canada

We consider a partially observable degrading system subject to condition monitoring and random failure. The system's condition is categorized into one of three states: a healthy state, a warning state, and a failure state. Only the failure state is observable. While the system is operational, vector data that is stochastically related to the system state is obtained through condition monitoring at regular sampling epochs. The state process evolution follows a hidden semi-Markov model (HSMM) and Erlang distribution is used for modeling the system's sojourn time in each of its operational states. The expectation-maximization (EM) algorithm is applied to estimate the state and observation parameters of the HSMM. Explicit formulas for several important quantities for the system residual life estimation such as the conditional reliability function and the mean residual life are derived in terms of the posterior probability that the system is in the warning state. Numerical examples are presented to demonstrate the applicability of the estimation procedure and failure prediction method. A comparison results with hidden Markov modeling are provided to illustrate the effectiveness of the proposed model. (c) 2015 Wiley Periodicals, Inc. Naval Research Logistics 62: 190-205, 2015

关键词： condition-based maintenance reliability mean residual life hidden semi-Markov model expectation-maximization algorithm

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An adaptive semiparametric and context-based approach to unsupervised change detection in multitemporal remote-sensing images

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2002年第4期11卷 452-466页

作者： Bruzzone, L Prieto, DF Univ Trent Dept Informat & Commun Technol I-38050 Trento Italy

In this paper, a novel automatic approach to the unsupervised identification of changes in multitemporal remote-sensing images is proposed. This approach, unlike classical ones, is based on the formulation of the unsupervised change-detection problem in terms of the Bayesian decision theory. In this context, an adaptive semiparametric technique for the unsupervised estimation of the statistical terms associated with the gray levels of changed and unchanged pixels in a difference image is presented. Such a technique exploits the effectivenesses of two theoretically well-founded estimation procedures: the reduced Parzen estimate (RPE) procedure and the expectation-maximization (EM) algorithm. Then, thanks to the resulting estimates and to a Markov Random Field (MRF) approach used to model the spatial-contextual information contained in the multitemporal images considered, a change detection map is generated. The adaptive semiparametric nature of the proposed technique allows its application to different kinds of remote-sensing images. Experimental results, obtained on two sets of multitemporal remote-sensing images acquired by two different sensors, confirm the validity of the proposed approach.

关键词： adaptive semiparametric estimation Bayes theory change detection expectation-maximization algorithm multitemporal images reduced Parzen estimate remote sensing

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A new multivariate zero-adjusted Poisson model with applications to biomedicine

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BIOMETRICAL JOURNAL 2019年第6期61卷 1340-1370页

作者： Liu, Yin Tian, Guo-Liang Tang, Man-Lai Yuen, Kam Chuen Zhongnan Univ Econ & Law Sch Stat & Math Wuhan 430073 Hubei Peoples R China Southern Univ Sci & Technol Dept Math Shenzhen 518055 Guangdong Peoples R China Hang Seng Management Coll Sch Decis Sci Dept Math & Stat Siu Lek YuenShatin Hong Kong Peoples R China Univ Hong Kong Dept Stat & Actuarial Sci Pokfulam Rd Hong Kong Peoples R China

Recently, although advances were made on modeling multivariate count data, existing models really has several limitations: (i) The multivariate Poisson log-normal model (Aitchison and Ho, 1989) cannot be used to fit multivariate count data with excess zero-vectors;(ii) The multivariate zero-inflated Poisson (ZIP) distribution (Li et al., 1999) cannot be used to model zero-truncated/deflated count data and it is difficult to apply to high-dimensional cases;(iii) The Type I multivariate zero-adjusted Poisson (ZAP) distribution (Tian et al., 2017) could only model multivariate count data with a special correlation structure for random components that are all positive or negative. In this paper, we first introduce a new multivariate ZAP distribution, based on a multivariate Poisson distribution, which allows the correlations between components with a more flexible dependency structure, that is some of the correlation coefficients could be positive while others could be negative. We then develop its important distributional properties, and provide efficient statistical inference methods for multivariate ZAP model with or without covariates. Two real data examples in biomedicine are used to illustrate the proposed methods.

关键词： data augmentation algorithm expectation-maximization algorithm hypothesis testing multivariate zero-adjusted Poisson stochastic representation

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Switching probabilistic slow feature extraction for semisupervised industrial inferential modeling

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JOURNAL OF PROCESS CONTROL 2024年 141卷

作者： Jiang, Chao Peng, Xin Huang, Biao Zhong, Weimin Beijing Univ Technol Beijing Artificial Intelligence Inst Fac Informat TechnolBeijing Key Lab Computat Inte Engn Res Ctr Digital CommunityMinist Educ Beijing 100124 Peoples R China Beijing Univ Technol Beijing Lab Intelligent Environm Protect Beijing 100124 Peoples R China East China Univ Sci & Technol Key Lab Smart Mfg Energy Chem Proc Minist Educ Shanghai 200237 Peoples R China Univ Alberta Dept Chem & Mat Engn Edmonton AB T6G 2G6 Canada

Predicting quality-relevant process variables is of paramount importance in optimizing and controlling chemical processes. Probabilistic Slow Feature Analysis (PSFA), a potent data-driven technique, plays a pivotal role in deducing quality indices by abstracting gradual variations in processes distinctly characterized by pronounced inertia. Nevertheless, PSFA's predictive efficacy encounters a substantial bottleneck due to the assumption of a single operating condition, compromising its accuracy, particularly in industries represented by switching operating conditions. To surmount this limitation, this study proposes an innovative approach that enriches PSFA with multi-operating condition process data and limited labels within a Bayesian framework, effectively combining continuous and discrete first-order Markov chains to capture the processes' inertia and dynamic shifts. The proposed method updates latent posterior distributions and model parameters iteratively via the expectation-maximization algorithm. The effectiveness of the proposed methodology is verified through a numerical case and industrial hydrocracking process data.

关键词： expectation-maximization algorithm Industrial hydrocracking process Multimode process Probabilistic slow feature analysis

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Joint segmentation of multivariate time series with hidden process regression for human activity recognition

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NEUROCOMPUTING 2013年 120卷 633-644页

作者： Chamroukhi, F. Mohammed, S. Trabelsi, D. Oukhellou, L. Amirat, Y. Univ Toulon & Var CNRS LSIS UMR 7296 F-83957 La Garde France Aix Marseille Univ CNRS ENSAM LSISUMR 7296 F-13397 Marseille France Univ Paris Est Creteil LISSI F-94400 Vitry Sur Seine France Univ Paris Est IFSTTAR GRETTIA F-93166 Noisy Le Grand France

The problem of human activity recognition is central for understanding and predicting the human behavior, in particular in a prospective of assistive services to humans, such as health monitoring, well being, security, etc. There is therefore a growing need to build accurate models which can take into account the variability of the human activities over time (dynamic models) rather than static ones which can have some limitations in such a dynamic context. In this paper, the problem of activity recognition is analyzed through the segmentation of the multidimensional time series of the acceleration data measured in the 3-d space using body-worn accelerometers. The proposed model for automatic temporal segmentation is a specific statistical latent process model which assumes that the observed acceleration sequence is governed by sequence of hidden (unobserved) activities. More specifically, the proposed approach is based on a specific multiple regression model incorporating a hidden discrete logistic process which governs the switching from one activity to another over time. The model is learned in an unsupervised context by maximizing the observed-data log-likelihood via a dedicated expectation-maximization (EM) algorithm. We applied it on a real-world automatic human activity recognition problem and its performance was assessed by performing comparisons with alternative approaches, including well-known supervised static classifiers and the standard hidden Markov model (HMM). The obtained results are very encouraging and show that the proposed approach is quite competitive even it works in an entirely unsupervised way and does not requires a feature extraction preprocessing step. (c) 2013 Elsevier B.V. All rights reserved.

关键词： Human activity recognition Acceleration data Times series segmentation Hidden process regression Unsupervised learning expectation-maximization algorithm

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Reliability assessment of multi-state phased-mission systems by fusing observation data from multiple phases of operation

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2019年 118卷 603-622页

作者： Xu, Zhaoping Mo, Yuchang Liu, Yu Jiang, Tao Univ Elect Sci & Technol China Sch Mech & Elect Engn Chengdu 611731 Sichuan Peoples R China Univ Elect Sci & Technol China Ctr Syst Reliabil & Safety Chengdu 611731 Sichuan Peoples R China Huaqiao Univ Fujian Prov Univ Key Lab Computat Sci Sch Math Sci Quanzhou 362021 Fujian Peoples R China

Multi-state phased-mission systems (MS-PMSs) are multi-state systems that intend to complete multiple, consecutive, and non-overlapping phases of operation with different functional requirements. Even though many computationally efficient tools have been developed to facilitate reliability modeling of MS-PMSs in the past few decades, reliability assessment of an MS-PMS by observation data is still a difficulty because of the state and phase dependencies. This paper devotes to addressing this challenge by putting forth a new method that can assess reliability of an MS-PMS by fusing observation data collected from multiple phases of operation. A dynamic Bayesian network (DBN) model is constructed to characterize the state dependence between an MS-PMS and its units as well as the phase dependence of each unit across consecutive phases. By putting observation data into the DBN model, the joint probability distributions of the nodes of the MS-PMS at any time slice can be updated, and with which the unknown parameters associated with the state transitions of all the units can be furtherly estimated by a tailored expectation-maximization (EM) algorithm. The reliability function of the MS-PMS can be, then, assessed by these estimates of all the units. Two illustrative examples, including a numerical case and a data collection robot are exemplified to demonstrate the effectiveness of the proposed method. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Multi-state phased-mission systems Dynamic Bayesian network Observation data Reliability assessment expectation-maximization algorithm

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Generalized endpoint-inflated binomial model

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COMPUTATIONAL STATISTICS & DATA ANALYSIS 2015年 89卷 97-114页

作者： Tian, Guo-Liang Ma, Huijuan Zhou, Yong Deng, Dianliang Univ Hong Kong Dept Stat & Actuarial Sci Hong Kong Hong Kong Peoples R China Univ Sci & Technol China CM0FL Hefei 230026 Anhui Peoples R China Chinese Acad Sci Acad Math & Syst Sci Beijing Peoples R China Shanghai Univ Finance & Econ Sch Stat & Management Shanghai Peoples R China Univ Regina Dept Math & Stat Regina SK S4S 0A2 Canada

To model binomial data with large frequencies of both zeros and right-endpoints, Deng and Zhang (in press) recently extended the zero-inflated binomial distribution to an endpoint-inflated binomial (EIB) distribution. Although they proposed the EIB mixed regression model, the major goal of Deng and Zhang (2015) is just to develop score tests for testing whether endpoint-inflation exists. However, the distributional properties of the EIB have not been explored, and other statistical inference methods for parameters of interest were not developed. In this paper, we first construct six different but equivalent stochastic representations for the EIB random variable and then extensively study the important distributional properties. Maximum likelihood estimates of parameters are obtained by both the Fisher scoring and expectation-maximization algorithms in the model without covariates. Bootstrap confidence intervals of parameters are also provided. Generalized and Fixed EIB regression models are proposed and the corresponding computational procedures are introduced. A real data set is analyzed and simulations are conducted to evaluate the performance of the proposed methods. All technical details are put in a supplemental document (see Appendix A). (C) 2015 Elsevier B.V. All rights reserved.

关键词： Endpoint-inflated binomial distribution expectation-maximization algorithm Multinomial logistic regression model Stochastic representation Zero-inflated binomial distribution

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