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bayesian algorithms for missing observations in experimental designs for a nanolubrication process

IIE TRANSACTIONS 2009年第11期41卷 969-978页

作者： Acharya, Navinchandra N. Nembhard, Harriet Black Penn State Univ Harold & Inge Marcus Dept Ind & Mfg En University Pk PA 16802 USA

Three new bayesian algorithms for missing observations based on predictive ability and minimization of the Residual Sum of Squares (RSS) are proposed. Their performance is compared to three existing algorithms based on an appropriate predicted residual error sum of squares statistic. Different positions of the missing observations and initial model conditions are considered. In all the investigated cases, the bayesian algorithms perform significantly better than non-bayesian algorithms. A numerical study is performed using a nanolubrication process. It shows that the bayesian complete RSS minimization algorithm yields the closest estimates of the missing observations, with the maximum predictive ability.

关键词： bayesian algorithms Analysis of Variance design of experiments nanomanufacturing Residual Sum of Squares

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Multiresolution and hybrid bayesian algorithms for automatic detection of change points

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IEE PROCEEDINGS-VISION IMAGE AND SIGNAL PROCESSING 1998年第4期145卷 280-286页

作者： MacDougall, S Nandi, AK Chapman, R Univ Strathclyde Dept Elect & Elect Engn Glasgow G1 1XW Lanark Scotland

Two methods for detection of step changes in noise corrupted piecewise-constant univariate datasets are presented. The aim is to determine automatically the number and position of any discontinuities in the mean. This problem is commonly known as the change-point problem. The multiresolution method presented involves performing a discrete wavelet transform, shrinking the coefficients via soft thresholding, and then correlating across scales. Also bayesian algorithms have long been available;they yield good results but they are impossible to apply in many cases due to huge computational complexity. The technique is compared with previously published hybrid bayesian algorithms. It is essential in any technique that the probability of false detections is low while retaining a sufficiently high probability of detection for correct change points. To this end the Student's t-test is introduced as a final stage after both methods. This eliminates most, if not all, false detections while retaining most correct ones. Simulation results are presented for each algorithm demonstrating that good performance is obtained for datasets with different characteristics.

关键词： change points bayesian algorithms multiresolution algorithm

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Distributed bayesian algorithms for fault-tolerant event region detection in wireless sensor networks

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IEEE TRANSACTIONS ON COMPUTERS 2004年第3期53卷 241-250页

作者： Krishnamachari, B Iyengar, S Univ So Calif Dept Elect Engn Syst Los Angeles CA 90089 USA Louisiana State Univ Dept Comp Sci Baton Rouge LA 70803 USA

We propose a distributed solution for a canonical task in wireless sensor networks-the binary detection of interesting environmental events. We explicitly take into account the possibility of sensor measurement faults and develop a distributed bayesian algorithm for detecting and correcting such faults. Theoretical analysis and simulation results show that 85-95 percent of faults can be corrected using this algorithm, even when as many as 10 percent of the nodes are faulty.

关键词： fault tolerance event detection sensor fusion bayesian algorithms wireless sensor networks

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Prediction of Machine Health Condition Using Neuro-Fuzzy and bayesian algorithms

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2012年第2期61卷 297-306页

作者： Chen, Chaochao Zhang, Bin Vachtsevanos, George Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30332 USA Impact Technol LLC Rochester NY 14623 USA

This paper proposes a novel approach for machine health condition prognosis based on neuro-fuzzy systems (NFSs) and bayesian algorithms. The NFS, after training with machine condition data, is employed as a prognostic model to forecast the evolution of the machine fault state with time. An online model update scheme is developed on the basis of the probability density function (PDF) of the NFS residuals between the actual and predicted condition data. bayesian estimation algorithms adopt the model's predicted data as prior information in combination with online measurements to update the degree of belief in the forecasting estimations. In order to simplify the implementation of the proposed approach, a recursive bayesian algorithm called particle filtering is utilized to calculate in real time a posterior PDF by a set of random samples (or particles) with associated weights. When new data become available, the weights of all particles are updated, and then, predictions are carried out, which form the PDF of the predicted estimations. The developed method is evaluated via two experimental cases-a cracked carrier plate and a faulty bearing. The prediction performance is compared with three prevalent machine condition predictors-recurrent neural networks, NFSs, and recurrent NFSs. The results demonstrate that the proposed approach can predict machine conditions more accurately.

关键词： bayesian algorithms machinery condition monitoring neuro-fuzzy systems (NFSs) prediction recurrent neural networks (RNNs) recurrent NFSs (RNFSs)

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Comments on "Distributed bayesian algorithms for fault-tolerant event region detection in wireless sensor networks"

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IEEE TRANSACTIONS ON COMPUTERS 2005年第9期54卷 1182-1183页

作者： Chen, QQ Lam, KY Fan, PZ SW Jiaotong Univ Inst Mobile Commun Chengdu 610031 Sichuan Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Hong Kong Peoples R China

In this correspondence, several errors related to the distributed bayesian algorithms for fault-tolerant event region detection in wireless sensor networks in [1] are spotted and corrected.

关键词： fault tolerance event region detection bayesian algorithms wireless sensor networks

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Salable bayesian algorithms for Quantitative Geosteering

Salable Bayesian Algorithms for Quantitative Geosteering

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作者： Miao, Yinsen Rice University

学位级别：Ph.D., Doctor of Philosophy

Geosteering is the iterative process of navigating the Bottom Hole Assembly (BHA) in a given geological setting in order to achieve pre-specified targets. To guide the directional drilling process, directional survey and logging-while-drilling (LWD) sensor measurements are used to estimate BHA position and the lateral changes of the geological structure. Two types of contemporary geosteering approaches, namely, model-based and stratification-based, are introduced. In the Chapter 1, we formulate the stratification-based approach as a bayesian optimization procedure: the log from a pilot reference well is used as a stratigraphic signature of the geological structure in a given region; the observed log sequence acquired along the wellbore is projected into the stratigraphic domain given a proposed earth model and directional survey; the pattern similarity between the converted log and the signature is measured by a correlation coefficient; then stochastic searching is performed on the space of all possible earth models to maximize the similarity under constraints of the prior understanding of the drilling process and target formation; finally inference is made based on the samples simulated from the posterior distribution using Stochastic Approximation Monte Carlo (SAMC). In chapter 2, we propose an efficient non-linear state space model approach to solve the model-based aspect of geosteering. This chapter is an extension to the chapter 1 whose limitations are further addressed here by taking the sequential nature of the acquired sensor measurements into account. For posterior inference of the latent states and model parameters, we apply extended Kalman filter, particle filter with Gibbs and particle filter with Metropolis Hasting. Our proposed methods consistently achieve good performance on synthetic datasets in term of high correlations between the interpreted log and reference log, and provides similar interpretations as the geosteering geologists on real wells. We

关键词： bayesian algorithms Quantitative geosteering Variable selection Count data Variational inference Particle filter

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An analysis of hippocampal spatio-temporal representations using a bayesian algorithm for neural spike train decoding

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IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 2005年第2期13卷 131-136页

作者： Barbieri, R Wilson, MA Frank, LM Brown, EN Massachusetts Gen Hosp Dept Anesthesia & Crit Care Neurosci Stat Res Lab Boston MA 02114 USA MIT Ctr Learning & Memory Cambridge MA 02139 USA MIT Dept Brain & Cognit Sci Cambridge MA 02139 USA Univ Calif San Francisco Dept Physiol Keck Ctr Integrat Neurosci San Francisco CA 94143 USA Harvard Univ MIT Sch Med Div Hlth Sci & Technol Cambridge MA 02139 USA

Neural spike train decoding algorithms are important tools for characterizing how ensembles of neurons represent biological signals. We present a bayesian neural spike train decoding algorithm based on a point process model of individual neurons, a linear stochastic state-space model of the biological signal, and a temporal latency parameter. The latency parameter represents the temporal lead or lag between the biological signal and the ensemble spiking activity. We use the algorithm to study Whether the representation of position by the ensemble spiking activity of pyramidal neurons in the CA1 region of the rat hippocampus is more consistent with prospective coding, i.e., future position, or retrospective coding, past position. Using 44 simultaneously recorded neurons and an ensemble delay latency of 400 ms, the median decoding error was 5.1 cm during 10 min of foraging in an open circular environment. The true coverage probability for the algorithm's 0.95 confidence regions was 0.71. These results illustrate how the bayesian neural spike train decoding paradigm may be used to investigate spatio-temporal representations of position by an ensemble of hippocampal neurons.

关键词： bayesian algorithms CA1 place cells decoding algorithms point process

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Performance of the bayesian online algorithm for the perceptron

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IEEE TRANSACTIONS ON NEURAL NETWORKS 2007年第3期18卷 902-905页

作者： de Oliveira, Evaldo Araujo Alamino, Roberto Castro Univ Sao Paulo Inst Astron Geofis & Ciencias Atmosfer BR-01060970 Sao Paulo Brazil Aston Univ Neural Comp Res Grp Birmingham B4 7ET W Midlands England

In this letter, we derive continuum equations for the generalization error of the bayesian online algorithm (BOnA) for the one-layer perceptron with a spherical covariance matrix using the Rosenblatt potential and show, by numerical calculations, that the asymptotic performance of the algorithm is the same as the one for the optimal algorithm found by means of variational methods with the added advantage that the BOnA does not use any inaccessible information during learning.

关键词： bayesian algorithms online gradient methods pattern classification

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bayesian Estimation in Stochastic Differential Equation Models via Laplace Approximation

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IFAC-PapersOnLine 2016年第7期49卷 1109-1114页

作者： Karimi, Hadiseh McAuley, Kimberley B. Department of Chemical Engineering Queen's University KingstonK7L3N6 Canada

A bayesian algorithm is developed for estimating measurement noise variances, disturbance intensities and model parameters in nonlinear stochastic differential equation (SDE) models of interest to chemical engineers. The proposed bayesian algorithm uses prior knowledge about parameters and builds on the Laplace Approximation Maximum Likelihood Estimation (LAMLE) algorithm (Karimi and McAuley, 2014). The effectiveness of the proposed algorithm is compared with LAMLE using a nonlinear continuous stirred tank reactor (CSTR) model. Parameter estimation using 2000 simulated datasets reveals that the proposed method provides more precise and less biased estimates, especially for small data sets. © 2016

关键词： Parameter estimation Approximation algorithms bayesian networks Differential equations Laplace transforms Maximum likelihood estimation Nonlinear analysis Nonlinear equations Spurious signal noise Stochastic models Stochastic systems Uncertainty analysis bayesian algorithms bayesian estimations Continuous stirred tank reactor Laplace approximation likelihood Simulated datasets Stochastic differential equation models Uncertain dynamic systems

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An Improved Estimation Using Polya-Gamma Augmentation for bayesian Structural Equation Models with Dichotomous Variables

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MEASUREMENT-INTERDISCIPLINARY RESEARCH AND PERSPECTIVES 2018年第2期16卷 81-91页

作者： Kim, Seohyun Lu, Zhenqiu Cohen, Allan S. Univ Georgia Educ Psychol Athens GA 30602 USA

bayesian algorithms have been used successfully in the social and behavioral sciences to analyze dichotomous data particularly with complex structural equation models. In this study, we investigate the use of the Polya-Gamma data augmentation method with Gibbs sampling to improve estimation of structural equation models with dichotomous variables. An empirical example is provided to illustrate the performance of different estimation approaches followed by a simulation study to evaluate the proposed method. The Polya-Gamma method is shown to provide stable results with larger effective sample size than standard Gibbs sampling.

关键词： Structural equation modeling bayesian algorithms Polya-Gamma distribution

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