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4th IEEE International Conference on Wireless Communications Signal Processing and Networking (WiSPNET) - Advancing Wireless and Mobile Communications Technologies for 2020 Information Society

作者： Menon, Aravind M. Eldho, Elizabeth Benny, Greeshma M. Sudarsan, Dhanya Muthoot Inst Technol & Sci Dept Comp Sci & Engn Kochi Kerala India

ISBN: (纸本)9781538692790

Hand written documents are one of the most important sources of writing media in ancient Kerala. Due to improper storage of manuscripts, about 80% of them got degraded. Noises are one of the most common degrading factor which affects the visibility of the images and makes them unclear. The main noises found in manuscripts are pepper salt noise, speckle noise and holes on billings etc. There are many denoising methods available. In this paper we have made a literature analysis, both basic and advanced algorithms. Finally compared the output of each methods, gibbs sampling technique gives more accurate output in handwritten manuscripts. But holes in documents were not removed. For this we have given an enhancement using Circle Hough Transform (CHT) Which detects the circles and can identify exact hole by comparing the boundary pixels of circle and background of the script. In future we can recognize the characters using Support Vector Machine(SVM) and Histogram of Orientation Gradients(HOG).

关键词： Hand written Manuscripts Denoising methods Circle Hough Transform gibbs sampling algorithm

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Intra-patient Arrhythmia Heartbeat Modeling by gibbs sampling 11th

Intra-patient Arrhythmia Heartbeat Modeling by Gibbs Samplin...

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11th Mexican Conference on Pattern Recognition (MCPR)

作者： Ramirez-Robles, Ethery Angel Jara-Maldonado, Miguel Etcheverry, Gibran Univ Americas Puebla Sta Catarina Martir Cholula Mexico

ISBN: (纸本)9783030210779;9783030210762

Heartbeat modeling allows to detect anomalies that reflect the functioning of the heart. Certain approaches face this problem by using Gaussian Mixture Models (GMMs) and other statistical classifiers by extracting the fiducial points provided by the MIT-BIH database. In this work, MIT-BIH database heartbeats are modeled into different heartbeat types from a single subject by using the gibbs sampling (GS) algorithm. Firstly, a data pre-processing step is performed;this step involves several tasks such as filtering the raw signals from the MIT-BIH database and reducing the heartbeat types to five. Secondly, the GS is applied to the resulting signals of one subject. Thirdly, the Euclidean distance between each heartbeat type is calculated, and lastly, the Bhattacharyya distance is used to classify heartbeats. The results obtained by the GS algorithm were also compared to results obtained by applying the Expectation Maximization (EM) algorithm to the same data-set. Results allow to conclude that GS is a proper solution for separating each heartbeat type;by providing a significant difference between each heartbeat type which can be used for classification.

关键词： Arrhythmia Electrocardiogram gibbs sampling algorithm Expectation Maximization QRS complex R programming

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Bayesian analysis of regression models with spatially correlated errors and missing observations

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COMPUTATIONAL STATISTICS & DATA ANALYSIS 2002年第4期39卷 387-400页

作者： Oh, MS Shin, DW Kim, HJ Ewha Womans Univ Dept Stat Seoul 120750 South Korea Korea Ocean Res & Dev Inst Ansan Kyung Gi Do South Korea

A Bayesian approach is proposed for estimating regression models on rectangular grids in which errors are spatially correlated and missing observations are present in the response variable. An easy and efficient Markov chain Monte Carlo algorithm is fully described for posterior inference on parameters and prediction of missing observations. Analysis of a real marine remote-sensing data set is presented to illustrate the method. (C) 2002 Elsevier Science B.V. All rights reserved.

关键词： gibbs sampling algorithm Markov chain Monte Carlo missing value posterior inference spatial data

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Statistical inference of Type-I progressively censored step-stress accelerated life test with dependent competing risks

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COMMUNICATIONS IN STATISTICS-THEORY AND METHODS 2022年第10期51卷 3077-3103页

作者： Bai, Xuchao Shi, Yimin Ng, Hon Keung Tony Xidian Univ Sch Econ & Management Xian 710126 Peoples R China Northwestern Polytech Univ Sch Math & Stat Xian Peoples R China Southern Methodist Univ Dept Stat Sci Dallas TX USA

This paper considers a step-stress accelerated dependent competing risks model under progressively Type-I censoring schemes. The dependence structure between competing risks is modeled by a general bivariate function, the cumulative exposure model is assumed and the accelerated model is described by the power rule model. The point and interval estimation of the model parameters and the reliability under normal usage level at mission time are obtained by using the maximum likelihood method and the asymptotic normal theory. We also consider the Bayesian estimators and the highest posterior density credible intervals based on conjugate priors, E-Bayesian, hierarchical Bayesian and empirical Bayesian methods. To illustrate the proposed methodology, the Marshall-Olkin bivariate exponential distribution is used to model the dependence structure between competing risks. A Monte Carlo simulation study and a real data analysis are presented to study the performance of different estimation methods.

关键词： Step-stress accelerated life test dependent competing risks power rule model cumulative exposure model Bayesian estimation gibbs sampling algorithm

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Bayesian inference for polyhazard models in the presence of covariates

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COMPUTATIONAL STATISTICS & DATA ANALYSIS 2001年第1期38卷 1-14页

作者： Mazucheli, J Louzada-Neto, F Achcar, JA Univ Estadual Maringa DEs Maringa Parana Brazil Univ Fed Sao Carlos DEs BR-13560 Sao Carlos SP Brazil Univ Sao Paulo ICMC BR-13560970 Sao Carlos SP Brazil

Polyhazard models are a flexible family for fitting lifetime data. Their main advantage over the single hazard models, such as the Weibull and the log-logistic models, is to include a large amount of nonmonotone hazard shapes, as bathtub and multimodal curves. The main goal of this paper is to present a Bayesian inference procedure for the polyhazard models in the presence of covariates, generalizing the Bayesian analysis presented in Berger and Sun (J. Amer. Statist. Assoc. 88 (1993) 1412), Basu et al. (J. Statist. Plan Inference 78 (1999) 255) and Kuo and Yang (Statist. Probab. Lett. 47 (2000) 229). The two most important particular polyhazard models, namely poly-Weibull. poly-log-logistic and a combination of both are studied in detail. The methodology is illustrated in two real medical datasets. (C) 2001 Elsevier Science B.V. All rights reserved.

关键词： Bayesian inference polyhazard models gibbs sampling algorithm arms algorithm artificial variables kaplan-meier survival curves bayes factor

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Slice sampling algorithm for estimating the item response theory model with ordinal response data

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COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION 2023年第10期52卷 4909-4932页

作者： Zhang, Jiwei Zhang, Zhaoyuan Lu, Jing Yunnan Univ Sch Math & Stat Kunming Yunnan Peoples R China Yili Normal Univ Sch Math & Stat Yili Peoples R China Northeast Normal Univ Sch Math & Stat Key Lab Appl Stat MOE Changchun 130024 Jilin Peoples R China

In this paper, a highly effective slice sampling algorithm is proposed to estimate the graded response model that has been widely used in educational psychological assessments. The new algorithm not only avoids the Metroplis-Hastings algorithm boring adjustment the turning parameters to achieve an appropriate acceptance probability, but also overcomes the dependence of the traditional gibbs sampling algorithm on the conjugate prior distribution. Three simulation studies are conducted and a detailed analysis of sexual compulsivity scale (SCS) data is carried out to further illustrate the proposed methodology.

关键词： Bayesian estimation Item response theory gibbs sampling algorithm Graded response model Metropolis-Hastings sampling algorithm Slice sampling algorithm

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Health assessment and prognostics based on higher-order hidden semi-Markov models

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NAVAL RESEARCH LOGISTICS 2021年第2期68卷 259-276页

作者： Liao, Ying Xiang, Yisha Wang, Min Texas Tech Univ Dept Ind Mfg & Syst Engn 2500 Broadway Lubbock TX 79409 USA Univ Texas San Antonio Dept Management Sci & Stat San Antonio TX USA

This paper presents a new and flexible prognostics framework based on a higher-order hidden semi-Markov model (HOHSMM) for systems or components with unobservable health states and complex transition dynamics. The HOHSMM extends the basic hidden Markov model (HMM) by allowing the hidden state to depend on its more distant history and assuming generally distributed state duration. An effective gibbs sampling algorithm is designed for statistical inference of the HOHSMM. We conduct a simulation study to evaluate the performance of the proposed HOHSMM sampler and examine the impacts of the distant-history dependency. We design a decoding algorithm to estimate the hidden health states using the learned model. Remaining useful life is predicted using a simulation approach given the decoded hidden states. The practical utility of the proposed prognostics framework is demonstrated by a case study on National Aeronautics and Space Administration (NASA) turbofan engines. We further compare the RUL prediction performance between the proposed HOHSMM and a benchmark mixture of Gaussians HMM prognostics method. The results show that the HOHSMM-based prognostics framework provides good hidden health-state assessment and RUL estimation for complex systems.

关键词： gibbs sampling algorithm higher-order hidden semi-Markov model prognostics remaining useful life

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Bayesian nonparametric forecasting for INAR models

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COMPUTATIONAL STATISTICS & DATA ANALYSIS 2016年 100卷 70-78页

作者： Bisaglia, Luisa Canale, Antonio Univ Turin Dept Econ & Stat Corso Unione Sovietica 218bis I-10134 Turin Italy Collegio Carlo Alberto Moncalieri Italy Univ Padua Dept Stat Sci I-35100 Padua Italy

A nonparametric Bayesian method for producing coherent predictions of count time series with the nonnegative integer-valued autoregressive process is introduced. Predictions are based on estimates of h-step-ahead predictive mass functions, assuming a nonparametric distribution for the innovation process. That is, the distribution of errors are modeled by means of a Dirichlet process mixture of rounded Gaussians. This class of prior has large support on the space and probability mass functions and can generate almost any kind of count distribution, including over/under-dispersion and multimodality. An efficient gibbs sampler is developed for posterior computation, and the method is used to analyze a dataset of visits to a web site. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Count time series INAR(1) Dirichlet process mixtures Forecasting gibbs sampling algorithm

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Markov modelling of changes in HIV-specific cytotoxic T-lymphocyte responses with time in untreated HIV-1 infected patients

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STATISTICS IN MEDICINE 2003年第10期22卷 1675-1690页

作者： Kousignian, I Autran, B Chouquet, C Calvez, V Gomard, E Katlama, C Rivière, Y Costagliola, D Hop La Pitie Salpetriere INSERM EMI 0214 F-75625 Paris 13 France Hop La Pitie Salpetriere INSERM U543 F-75625 Paris France Hop La Pitie Salpetriere Virol Lab F-75625 Paris 13 France Hop La Pitie Salpetriere Serv Malad Infect & Trop F-75625 Paris 13 France CHU Cochin Port Royal INSERM U445 Paris France Inst Pasteur Dept Sida & Retrovirus Paris France CNRS UMR C5583 Lab Stat & Probabil Toulouse France

HIV-specific cytotoxic CD8(+) T-lymphocytes (CTL) appear to be the cornerstone of the immune response to HIV infection. Recent studies show that CTL activity reflects patients' anti-HIV immune status and slows disease progression. However, the dynamics of the diversity of this response also appears as a key parameter for immune control but the dynamics of this diversity is largely undocumented. We modelled changes in CTL responses against the seven principal HIV proteins over time. We also studied the influence of plasma viral load on temporal changes in HIV protein recognition by memory CTL. The generic model we developed is based on a continuous time homogeneous Markov process with reversible states. Those states are defined by the number of proteins recognized by memory CTL in a given patient at a given time. This approach was developed within a Bayesian framework. Full Bayesian inference is implemented using Markov chain Monte Carlo simulations (MCMC). The gibbs sampling algorithm was used to estimate the marginal posterior distributions of the transition intensities between stages of CTL responses. We applied our model to data of 152 HIV-infected patients included in the IMMUNOCO cohort. The model suggested that the diversity of HIV protein recognition by memory CTL in treatment-naive patients decreases as the disease progresses. Namely, the loss of T cytotoxic responses is globally faster than their acquisition. Indeed, these patients' T cytotoxic responses were characterized by marked individual turnover and a gradual loss of multiple protein recognition over time, this loss accelerating as viral load increased. Copyright (C) 2003 John Wiley Sons, Ltd.

关键词： cytotoxic T-lymphocytes (CTL) Markov process Bayesian inference Monte Carlo Markov chains (MCMC) gibbs sampling algorithm

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Application with a hybrid ant colony optimisation in motif detecting problem

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INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY 2012年第2期44卷 88-93页

作者： Zhang, Yi Zhang, Meng Pei, Zhili Jilin Business & Technol Coll Dept Comp Sci Changchun 130062 Peoples R China Air Force Aviat Univ Mil Simulat Technol Inst Changchun 130022 Peoples R China Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China Jilin Univ Key Lab Symbol Computat & Knowledge Engn Minist E Changchun 130012 Peoples R China Inner Mongolia Univ Nationalities Coll Comp Sci & Technol Tongliao 028000 Peoples R China Jilin Univ Coll Math Changchun 130012 Peoples R China

In this paper, a hybrid optimisation algorithm for the motif detection problem of biological sequences is presented. Our method is improved gibbs sampling method by employing an improved ant colony optimisation (ACO) algorithm. The goal of our method is to reduce the required computing time and get better solution. First, we find a set of better candidate positions for revising the motif by using an improved ACO. Then we use these candidate positions as the input to the gibbs sampling method. The simulation results show that by employing our improved algorithm, both efficiency and quality for detecting motifs are improved compared with simple gibbs sampling method.

关键词： ant colony optimisation gibbs sampling algorithm motif detection problem

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