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arXiv 2024年

作者： Azizi, Ilia Boldi, Marc-Olivier Chavez-Demoulin, Valérie HEC University of Lausanne Lausanne Switzerland Lausanne Switzerland

This work introduces the Supervised expectation-maximization Framework (SEMF), a versatile and model-agnostic approach for generating prediction intervals in datasets with complete or missing data. SEMF extends the expectation-maximization algorithm, traditionally used in unsupervised learning, to a supervised context, leveraging latent variable modeling for uncertainty estimation. Extensive empirical evaluations across 11 tabular datasets show that SEMF often achieves narrower normalized prediction intervals and higher coverage rates than traditional quantile regression methods1. Furthermore, SEMF can be integrated with machine learning models like gradient-boosted trees and neural networks, highlighting its practical applicability. The results indicate that SEMF enhances uncertainty quantification, particularly in scenarios with complete data. © 2024, CC BY.

关键词： expectation maximization algorithm

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Fault reconnaissance agent for sensor networks

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MOBILE INFORMATION SYSTEMS 2010年第3期6卷 229-247页

作者： Shakshuki, Elhadi M. Xing, Xinyu Sheltami, Tarek R. Acadia Univ Jodrey Sch Comp Sci Wolfville NS B4P 2R6 Canada Univ Colorado Dept Comp Sci Boulder CO 80309 USA King Fahd Univ Petr & Minerals Dept Comp Engn Dhahran 31261 Saudi Arabia

One of the key prerequisite for a scalable, effective and efficient sensor network is the utilization of low-cost, low-overhead and high-resilient fault-inference techniques. To this end, we propose an intelligent agent system with a problem solving capability to address the issue of fault inference in sensor network environments. The intelligent agent system is designed and implemented at base-station side. The core of the agent system - problem solver - implements a fault-detection inference engine which harnesses expectation maximization (EM) algorithm to estimate fault probabilities of sensor nodes. To validate the correctness and effectiveness of the intelligent agent system, a set of experiments in a wireless sensor testbed are conducted. The experimental results show that our intelligent agent system is able to precisely estimate the fault probability of sensor nodes.

关键词： Management intelligent agents expectation maximization algorithm wireless sensor networks

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Parameter Estimation for a k-out-of-n: F System

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COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION 2014年第1期43卷 99-114页

作者： Duan Qihong Liu Junrong Xi An Jiao Tong Univ Sch Math & Stat Dept Stat Xian 710049 Shaanxi Peoples R China NW Univ Xian Dept Math Xian 710069 Shaanxi Peoples R China

We propose an expectation maximization algorithm to estimate the parameters of a k-out-of-n: F system. Components composing the system are independent and have exponential duration distributions. As an ideal model of a practical system, we cannot observe any component's duration. The observation consists of lifetime data of the system. Based on a construction of a continuous time Markov chain (CTMC) for the system, we determine some probabilities and expectations conditional on the lifetime of the system. Then, we use an expectation maximization algorithm to estimate the system's parameters. We carry out simulation studies to test the accuracy of the algorithm.

关键词： Continuous time Markov chain expectation maximization algorithm k-out-of-n: F system Parameter estimation

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Exact finite-dimensional filters for maximum likelihood parameter estimation of continuous-time linear Gaussian systems

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SIAM JOURNAL ON CONTROL AND OPTIMIZATION 1997年第6期35卷 1908-1923页

作者： Elliott, RJ Krishnamurthy, V UNIV MELBOURNE DEPT ELECT & ELECT ENGNPARKVILLEVIC 3052AUSTRALIA

In this paper, we derive a new class of finite-dimensional filters for integrals and stochastic integrals of moments of the state for continuous-time linear Gaussian systems. Apart from being of significant mathematical interest, these new filters can be used with the expectation maximization (EM) algorithm to yield maximum likelihood estimates of the model parameters.

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

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Operating Condition Diagnosis Based on HMM with Adaptive Transition Probabilities in Presence of Missing Observations

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AICHE JOURNAL 2015年第2期61卷 477-493页

作者： Sammaknejad, Nima Huang, Biao Xiong, Weili Fatehi, Alireza Xu, Fangwei Espejo, Aris Univ Alberta Dept Chem & Mat Engn Edmonton AB T6G 2V4 Canada Jiangnan Univ Key Lab Adv Proc Control Light Ind Minist Educ Wuxi 214122 Jiangsu Peoples R China KN Toosi Univ Technol Fac Elect Engn Tehran Iran Syncrude Canada Ltd Ft Mcmurray AB T9H 3L1 Canada

A new approach for modeling and monitoring of the multivariate processes in presence of faulty and missing observations is introduced. It is assumed that operating modes of the process can transit to each other following a Markov chain model. Transition probabilities of the Markov chain are time varying as a function of the scheduling variable. Therefore, the transition probabilities will be able to vary adaptively according to different operating modes. In order to handle the problem of missing observations and unknown operating regimes, the expectation maximization algorithm is used to estimate the parameters. The proposed method is tested on two simulations and one industrial case studies. The industrial case study is the abnormal operating condition diagnosis in the primary separation vessel of oil-sand processes. In comparison to the conventional methods, the proposed method shows superior performance in detection of different operating conditions of the process. (c) 2014 American Institute of Chemical Engineers AIChE J, 61: 477-493, 2015

关键词： fault diagnosis hidden Markov models adaptive transition probabilities expectation maximization algorithm

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QUEUEING SYSTEMS 2017年第3-4期85卷 337-359页

作者： Buchholz, Peter Kriege, Jan TU Dortmund Informat 4 D-44221 Dortmund Germany

In this paper, we consider a queue where the inter-arrival times are correlated and, additionally, service times are also correlated with inter-arrival times. We show that the resulting model can be interpreted as an MMAP[K]/PH[K]/1 queue for which matrix geometric solution algorithms are available. The major result of this paper is the presentation of approaches to fit the parameters of the model, namely the MMAP, the PH distribution and the parameters introducing correlation between inter-arrival and service times, according to some trace of inter-arrival and corresponding service times. Two different algorithms are presented. The first algorithm is based on available methods to compute a MAP from the inter-arrival times and a PH distribution from the service times. Afterward, the correlation between inter-arrival and service times is integrated by solving a quadratic programming problem over some joint moments. The second algorithm is of the expectation maximization type and computes all parameters of the MAP and the PH distribution in an iterative way. It is shown that both algorithms yield sufficiently accurate results with an acceptable effort.

关键词： Markovian arrival process Marked Markovian arrival processes Phase type distributions Multi-class queues expectation maximization algorithm

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Adaptive Central Difference Kalman Filter With Unknown Measurement Noise Covariance and Its Application to Airborne POS

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IEEE SENSORS JOURNAL 2021年第8期21卷 9927-9936页

作者： Liu, Yanhong Ning, Xiaolin Li, Jianli Ye, Wen Wang, Bo Ma, Xin Beihang Univ Res Inst Frontier Sci Beijing 100191 Peoples R China Natl Inst Metrol Beijing 102200 Peoples R China

Position and Orientation system (POS), a loosely integrated inertial navigation system (INS) and global positioning system (GPS), can provide high-accuracy motion information for the airborne remote sensing loads, which plays a crucial role in airborne remote sensing imaging. However, the airborne POS often suffers from the harsh environment, such as aircraft maneuver mode and other external disturbance, which will lead to measurement noise unknown and further affects the accuracy of motion parameters. In this paper, an adaptive central difference Kalman filter method based on expectation maximization algorithm is proposed, which can estimate measurement noise adaptively and further improve the performance of POS. A flight experiment is conducted and the results show that the proposed method achieves higher-accuracy motion information by compared with the traditional CDKF method and covariance matching.

关键词： Noise measurement Global Positioning System Mathematical model Kalman filters Sensors Estimation Navigation Airborne remote sensing imaging position and orientation system expectation maximization algorithm central difference Kalman filter

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Partitioning the distribution function of radar reflectivity in convective storms using maximum likelihood method

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ATMOSPHERIC RESEARCH 2013年 124卷 123-136页

作者： Diop, Cheikh Abdoulahat Sauvageot, Henri Mesnard, Frederic Univ Toulouse Observ Midi Pyrenees Lab Aerol Toulouse France Univ Toulouse GEII Inst Univ Technol Tarbes Tarbes France

A method to estimate the parameters of radar reflectivity distribution functions of convective storm systems is presented. To carry out this estimation, the probability density distribution of the radar reflectivity, P(Z), is computed using data collected on continental convective storm systems with the radar of Little Rock in central Arkansas. We show that P(Z) can be modeled as a mixture of Gaussian components, each of them corresponding to a type of precipitation. The EM (expectation maximization) algorithm is used to decompose P(Z) in these merged components. In the precipitation associated with intense continental convective storms, four main populations are considered: shallow precipitation, stratiform precipitation, convective precipitation, and hail. Each component is described by the fraction of area occupied inside P(Z) and by the Gaussian parameters, mean and variance. The retrieval of the mixed distribution by a linear combination of the Gaussian components gives a very satisfactory P(Z) fitting. It is shown that this method enables to follow the evolution with time of the various precipitation components of a convective system crossing the radar observed area. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Precipitation Radar reflectivity of precipitation Hail detection by radar Probability density function of precipitation NEXRAD radar network Hailstorm observation by radar expectation maximization algorithm

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Bayesian perspective for the registration of multiple 3D views

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COMPUTER VISION AND IMAGE UNDERSTANDING 2014年 118卷 84-96页

作者： Mateo, X. Orriols, X. Binefa, X. Univ Autonoma Barcelona Dept Comp Sci E-08193 Barcelona Spain Microsoft Search Technol Ctr Asia Beijing Peoples R China Univ Pompeu Fabra Dept Informat & Commun Technol Barcelona Spain

The registration of multiple 3D structures in order to obtain a full-side representation of a scene is a longtime studied subject. Even if the multiple pairwise registrations are almost correct, usually the concatenation of them along a cycle produces a non-satisfactory result at the end of the process due to the accumulation of the small errors. Obviously, the situation can still be worse if, in addition, we have incorrect pairwise correspondences between the views. In this paper, we embed the problem of global multiple views registration into a Bayesian framework, by means of an expectation-maximization (EM) algorithm, where pairwise correspondences are treated as missing data and, therefore, inferred through a maximum a posteriori (MAP) process. The presented formulation simultaneously considers uncertainty on pairwise correspondences and noise, allowing a final result which outperforms, in terms of accuracy and robustness, other state-of-the-art algorithms. Experimental results show a reliability analysis of the presented algorithm with respect to the percentage of a priori incorrect correspondences and their consequent effect on the global registration estimation. This analysis compares current state-of-the-art global registration methods with our formulation revealing that the introduction of a Bayesian formulation allows reaching configurations with a lower minimum of the global cost function. (C) 2013 Elsevier Inc. All rights reserved.

关键词： 3D registration Pairwise correspondences expectation maximization algorithm

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Multiple model approach to nonlinear system identification with an uncertain scheduling variable using EM algorithm

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JOURNAL OF PROCESS CONTROL 2013年第10期23卷 1480-1496页

作者： Chen, Lei Tulsyan, Aditya Huang, Biao Liu, Fei Jiangnan Univ Inst Automat Key Lab Adv Proc Control Light Ind Minist Educ Wuxi 214122 Peoples R China Univ Alberta Dept Chem & Mat Engn Edmonton AB T6G 2G6 Canada

This paper deals with system identification of general nonlinear dynamical systems with an uncertain scheduling variable. A multi model approach is developed;wherein, a set of local auto regressive exogenous (ARX) models are first identified at different process operating points, and are then combined to describe the complete dynamics of a nonlinear system. An expectation-maximization (EM) algorithm is used for simultaneous identification of local ARX models, and for computing the probability associated with each of the local ARX models taking effect. A smoothing algorithm is used to estimate the distribution of the hidden scheduling variables in the EM algorithm. If the dynamics of the scheduling variables are linear, Kalman smoother is used;whereas, if the dynamics are nonlinear, sequential Monte-Carlo (SMC) method is used. Several simulation examples, including a continuous stirred tank reactor (CSTR) and a distillation column, are considered to illustrate the efficacy of the proposed method. Furthermore, to highlight the practical utility of the developed identification method, an experimental study on a pilot-scale hybrid tank system is also provided. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： System identification Nonlinear process Multiple models expectation maximization algorithm Kalman smoother Particle smoother

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