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Segmented Sequence Prediction Using Variable-Order Markov model Ensemble

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2025年第3期37卷 1425-1438页

作者： Yan, Weichao Ma, Hao Yang, Zaiyue Southern Univ Sci & Technol Guangdong Prov Key Lab Human Augmentat & Rehabil R Sch Automat & Intelligent Mfg Shenzhen Key Lab Control Theory & Intelligent Syst Shenzhen 518055 Peoples R China

In recent years, sequence prediction, particularly in natural language processing tasks, has made significant progress due to advanced neural network architectures like Transformer and enhanced computing power. However, challenges persist in modeling and analyzing certain types of sequence data, such as human daily activities and competitive ball games. These segmented sequence data are characterized by short length, varying local dependencies, and coarse-grained unit states. These characteristics limit the effectiveness of conventional probabilistic graphical models and attention-based or recurrent neural networks in modeling and analyzing segmented sequence data. To address this gap, we introduce a novel generative model for segmented sequences, employing an ensemble of multiple variable-order Markov models (VOMMs) to flexibly represent state transition dependencies. Our approach integrates probabilistic graphical models with neural networks, surpassing the representation capabilities of single high-order or variable-order Markov models. Compared to end-to-end deep learning models, our method offers improved interpretability and reduces overfitting in short segments. We demonstrate the efficacy of our proposed method in two tasks: predicting tennis shot types and forecasting daily action sequences. These applications highlight the broad applicability of our segmented sequence modeling approach across diverse domains.

关键词： Predictive models Hidden Markov models Data models Sports Natural language processing Analytical models Transformers Training Recurrent neural networks graphical models Variable-order Markov model sequence prediction probabilistic graphical model recurrent neural network

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probabilistic graphical models in energy systems:A review

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Building Simulation 2022年第5期15卷 699-728页

作者： Tingting Li Yang Zhao Ke Yan Kai Zhou Chaobo Zhang Xuejun Zhang Institute of Refrigeration and Cryogenics Zhejiang UniversityHangzhouChina Department of Building School of Design and EnvironmentNational University of Singapore4 Architecture DriveSingapore Zhejiang Energy Gas Group Co. LtdHangzhouChina

probabilistic graphical models(PGMs)can effectively deal with the problems of energy consumption and occupancy prediction,fault detection and diagnosis,reliability analysis,and optimization in energy *** with the black-box models,PGMs show advantages in model interpretability,scalability and *** have great potential to realize the true artificial intelligence in energy systems of the next *** paper intends to provide a comprehensive review of the PGM-based approaches published in the last *** reveals the advantages,limitations and potential future research directions of the PGM-based approaches for energy *** types of PGMs are summarized in this review,including static models(SPGMs)and dynamic models(DPGMs).SPGMs can conduct probabilistic inference based on incomplete,uncertain or even conflicting ***-based approaches are proposed to deal with various management tasks in energy *** show outstanding performance in fault detection and diagnosis of energy *** can represent a dynamic and stochastic process by describing how its state changes with ***-based approaches have high accuracy in predicting the energy consumption,occupancy and failures of energy *** the future,a unified framework is suggested to fuse the knowledge-driven and data-driven PGMs for achieving better *** PGM-based approaches are needed that can be adapted to various energy *** algorithms would outperform the basic PGMs by integrating advanced techniques such as deep learning and first-order logic.

关键词： probabilistic graphical model energy system Bayesian network-dynamic Bayesian network Markov chain hidden Markov model

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probabilistic graphical models for Flood State Detection of Roads Combining Imagery and DEM

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2012年第6期9卷 1051-1055页

作者： Frey, Daniel Butenuth, Matthias Straub, Daniel Tech Univ Munich Chair Remote Sensing Technol D-80333 Munich Germany Tech Univ Munich Engn Risk Anal Grp D-80333 Munich Germany

A new system for estimating the state of roads during flooding based on probabilistic graphical models is presented. The location of the roads is given by a geographic information system, whereas the up-to-date information for the assessment of flood state is delivered by remote sensing data. Furthermore, the height information from a digital elevation model (DEM) is combined with image data to improve the accuracy of the results. The presented system is based on factor graphs, which are used to model the statistical dependence between random variables. Three different models are presented: a 1-D pixel-based model, a 2-D topology-based model considering the dependences of neighboring pixels, and a 3-D multitemporal-based model, which can deal with sequential remote sensing imagery at several points in time. The proposed models are compared to a flood simulation based only on the DEM and a maximum likelihood classification based only on the image data. A numerical evaluation demonstrates the improved performance of the three proposed models.

关键词： Bayesian network (BN) detection factor graph flooding probabilistic graphical model

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Distributed Cognitive Radio Network Management via Algorithms in probabilistic graphical models

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IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2011年第2期29卷 338-348页

作者： Liang, Yingbin Lai, Lifeng Halloran, John Syracuse Univ Dept Elect Engn & Comp Sci Syracuse NY 13244 USA Univ Arkansas Dept Syst Engn Little Rock AR 72204 USA Univ Washington Dept Elect Engn Seattle WA 98195 USA

In this paper, cognitive radio wireless networks are investigated, in which a number of primary users (PUs) transmit in orthogonal frequency bands, and a number of secondary users (SUs) monitor the transmission status of the PUs and search for transmission opportunities in these frequency bands by collaborative detection. A network management problem is formulated to find the configuration of SUs (assignment of SUs) to detect PUs so that the best overall network performance is achieved. Two performance metrics are considered, both of which characterize the probability of errors for detecting transmission status of all PUs. For both metrics, a graphical representation of the problem is provided, which facilitates to connect the problems under study to the sum-product inference problem studied in probabilistic graphical models. Based on the elimination algorithm that solves the sum-product problem, a message passing algorithm is proposed to solve the problem under study in a computationally efficient manner and in a distributed fashion. The complexity of the algorithm is shown to be significantly lower than that of the exhaustive search approach. Moreover, a clique-tree algorithm is applied to efficiently compute the impacts of each SU's choice on the overall system performance. Finally, simulation results are provided to demonstrate the considerable performance enhancement achieved by implementing an optimal assignment of SUs.

关键词： Cognitive radio collaborative detection distributed algorithm message passing algorithm probabilistic graphical model

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Collective game behavior learning with probabilistic graphical models

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NEUROCOMPUTING 2016年 194卷 74-86页

作者： Qin, Zengchang Khawar, Farhan Wan, Tao Beihang Univ Sch Automat Sci & Elect Engn Intelligent Comp & Machine Learning Lab Beijing 100191 Peoples R China Hong Kong Univ Sci & Technol Dept Comp Sci & Engn Hong Kong Hong Kong Peoples R China Beihang Univ Dept Biol Sci & Med Engn Beijing 100191 Peoples R China

The minority game is a simple game theory model for describing the collective behavior of agents in an idealized situation where they compete for some finite resources. In this paper, we assume that collective behavior is generated by the aggregation of independent actions of agents and the action follows the minority game. A probabilistic machine learning model is proposed to model the generative process of how collective behavior emerges from individual actions. By training on collective data, we can infer the most likely parameters use the trained system to make predictions. This model can be regarded as a new learning paradigm of analyzing collective data by decomposing the generative process into independent micro-level games. To demonstrate the effectiveness of the model, we conduct experiments on an artificial data set and the real-world data. A set of selected stock indices are tested to capture their rises and falls in the market. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Minority game probabilistic graphical model Stock index prediction Collective intelligence Behavior decomposition

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Dynamic Hybrid Random Fields for the probabilistic graphical modeling of Sequential Data: Definitions, Algorithms, and an Application to Bioinformatics

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NEURAL PROCESSING LETTERS 2018年第2期48卷 733-768页

作者： Bongini, Marco Freno, Antonino Laveglia, Vincenzo Trentin, Edmondo Univ Siena DIISM Via Roma 56 I-53100 Siena Italy Zalando SE Charlottenstr 4 D-10969 Berlin Germany Univ Firenze DINFO Via S Marta 3 I-50139 Florence Italy

The paper introduces a dynamic extension of the hybrid random field (HRF), called dynamic HRF (D-HRF). The D-HRF is aimed at the probabilistic graphical modeling of arbitrary-length sequences of sets of (time-dependent) discrete random variables under Markov assumptions. Suitable maximum likelihood algorithms for learning the parameters and the structure of the D-HRF are presented. The D-HRF inherits the computational efficiency and the modeling capabilities of HRFs, subsuming both dynamic Bayesian networks and Markov random fields. The behavior of the D-HRF is first evaluated empirically on synthetic data drawn from probabilistic distributions having known form. Then, D-HRFs (combined with a recurrent autoencoder) are successfully applied to the prediction of the disulfide-bonding state of cysteines from the primary structure of proteins in the Protein Data Bank.

关键词： probabilistic graphical model Hybrid random field Dynamic Bayesian network Recurrent autoencoder Disulfide bond

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A review on probabilistic graphical models in evolutionary computation

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JOURNAL OF HEURISTICS 2012年第5期18卷 795-819页

作者： Larranaga, Pedro Karshenas, Hossein Bielza, Concha Santana, Roberto Univ Politecn Madrid Fac Informat Computat Intelligence Grp E-28660 Madrid Spain Univ Basque Country Dept Comp Sci & Artificial Intelligence Intelligent Syst Grp San Sebastin 20080 Donostia Spain

Thanks to their inherent properties, probabilistic graphical models are one of the prime candidates for machine learning and decision making tasks especially in uncertain domains. Their capabilities, like representation, inference and learning, if used effectively, can greatly help to build intelligent systems that are able to act accordingly in different problem domains. Evolutionary algorithms is one such discipline that has employed probabilistic graphical models to improve the search for optimal solutions in complex problems. This paper shows how probabilistic graphical models have been used in evolutionary algorithms to improve their performance in solving complex problems. Specifically, we give a survey of probabilistic model building-based evolutionary algorithms, called estimation of distribution algorithms, and compare different methods for probabilistic modeling in these algorithms.

关键词： probabilistic graphical model Bayesian network Evolutionary computation Estimation of distribution algorithm

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Deep probabilistic graphical modeling for robust multivariate time series anomaly detection with missing data

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2023年第1期238卷

作者： Yang, Jingyu Yue, Zuogong Yuan, Ye Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automation Key Lab Image Proc & Intelligent Control Minist Educ Wuhan 430074 Peoples R China

Multivariate time series anomaly detection with missing data is one of the most pending issues for industrial monitoring. Due to scarcity of labeled anomalies, most advanced data-driven anomaly detection approaches fall in the unsupervised learning paradigm. As a premise in the presence of missing data, one needs to improve the data quality through data imputation with a separate model. Our concern lies in the consistency between data imputation and unsupervised learning for robust anomaly detection, regarding accurately discovering the spatiotemporal dependence among multiple variables over time. However, the existing practice tends to overlook this consistency and decouple the training process for these two closely linked tasks. This article novelly proposes a probabilistic multivariate time series anomaly detection framework that unifies data imputation and unsupervised learning. A deep probabilistic graphical model abbreviated SCNF is first devised for unsupervised density estimation. A tailored expectation maximization-based optimization scheme is then developed to achieve the joint training of data imputation and unsupervised learning with missing data. The efficacy is experimentally corroborated in several industrial applications, including chemical process, water treatment and network traffic. Briefly, the joint training framework enhances the AUROC of SCNF by averagely 6.34% for three applications under 50% data missing rate.

关键词： Multivariate time series Anomaly detection Missing data probabilistic graphical model Expectation maximization

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Incorporating structured assumptions with probabilistic graphical models in fMRI data analysis

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NEUROPSYCHOLOGIA 2020年第0期144卷 107500-107500页

作者： Cai, Ming Bo Shvartsman, Michael Wu, Anqi Zhang, Hejia Zhu, Xia Univ Tokyo Int Res Ctr Neurointelligence WPI IRCN UTIAS Tokyo Japan Princeton Univ Princeton Neurosci Inst Princeton NJ 08544 USA Facebook Real Labs Pittsburgh PA USA Columbia Univ Mortimer B Zuckerman Mind Brain Behav Inst New York NY 10027 USA Princeton Univ Dept Elect Engn Princeton NJ 08544 USA Intel Corp Santa Clara CA 95051 USA

With the wide adoption of functional magnetic resonance imaging (fMRI) by cognitive neuroscience researchers, large volumes of brain imaging data have been accumulated in recent years. Aggregating these data to derive scientific insights often faces the challenge that fMRI data are high-dimensional, heterogeneous across people, and noisy. These challenges demand the development of computational tools that are tailored both for the neuroscience questions and for the properties of the data. We review a few recently developed algorithms in various domains of fMRI research: fMRI in naturalistic tasks, analyzing full-brain functional connectivity, pattern classification, inferring representational similarity and modeling structured residuals. These algorithms all tackle the challenges in fMRI similarly: they start by making clear statements of assumptions about neural data and existing domain knowledge, incorporate those assumptions and domain knowledge into probabilistic graphical models, and use those models to estimate properties of interest or latent structures in the data. Such approaches can avoid erroneous findings, reduce the impact of noise, better utilize known properties of the data, and better aggregate data across groups of subjects. With these successful cases, we advocate wider adoption of explicit model construction in cognitive neuroscience. Although we focus on fMRI, the principle illustrated here is generally applicable to brain data of other modalities.

关键词： probabilistic graphical model Bayesian fMRI Cognitive neuroscience Big data Factor model Matrix normal

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Lane Detection and Tracking based on Fully Convolutional Networks and probabilistic graphical models

Lane Detection and Tracking based on Fully Convolutional Net...

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Thanh-Phat Nguyen Vu-Hoang Tran Ching-Chun Huang Natl Chung Cheng Univ Dept Elect Engn Chiayi Taiwan Natl Chung Cheng Univ Adv Inst Mfg High Tech Innovat Chiayi Taiwan CIRAS Chiayi Taiwan Ho Chi Minh Univ Technol & Educ Ho Chi Minh City Vietnam

ISBN: (纸本)9781538666500

In this paper, we proposed a systematic algorithm that integrates a fully convolutional network (FCN) for lane detection and a probabilistic graphical model for lane tracking. The proposed method consists of three main steps: lane candidate extraction, multi-component lane modeling, and multi-component lane tracking. The lane extraction step detects lane candidates by fusing low-level lane edges extracted by traditional image processing and high-level lane segments extracted by the designed FCN. After lane candidates are detected and clustered, we divide each lane into n components and represent lane components plus whole lanes as nodes in the Hough Transform domain. That is, we model each lane by a link structure with n+1 nodes. Finally, a probabilistic graphical model is built to track multiple lanes through frames in the Hough domain at the same time. The experimental results show that our method can track lanes in various challenging conditions of typical urban scenes such as curved lanes, texture marking, and lane occlusion. For the comparison, we evaluated our system by Caltech Lane Datasets and shown better performance in terms of metrics than previous techniques.

关键词： Lane detection probabilistic graphical model Segmentation Deep Neural Network Inverse Perspective Mapping

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