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bayesian multi-QTL mapping for growth curve parameters

BMC proceedings 2010年第1期4 Suppl 1卷 S12页

作者： Henri C M Heuven Luc L G Janss Clinical Sciences of Companion Animals Faculty of Veterinary Medicine Utrecht University PO box 80163 3508 TD Utrecht The Netherlands . h.c.m.heuven@uu.nl.

BACKGROUND:Identification of QTL affecting a phenotype which is measured multiple times on the same experimental unit is not a trivial task because the repeated measures are not independent and in most cases show a trend in time. A complicating factor is that in most cases the mean increases non-linear with time as well as the variance. A two- step approach was used to analyze a simulated data set containing 1000 individuals with 5 measurements each. First the measurements were summarized in latent variables and subsequently a genome wide analysis was performed of these latent variables to identify segregating QTL using a bayesian algorithm. RESULTS:For each individual a logistic growth curve was fitted and three latent variables: asymptote (ASYM), inflection point (XMID) and scaling factor (SCAL) were estimated per individual. Applying an 'animal' model showed heritabilities of approximately 48% for ASYM and SCAL while the heritability for XMID was approximately 24%. The genome wide scan revealed four QTLs affecting ASYM, one QTL affecting XMID and four QTLs affecting SCAL. The size of the QTL differed. QTL with a larger effect could be more precisely located compared to QTL with small effect. The locations of the QTLs for separate parameters were very close in some cases and probably caused the genetic correlation observed between ASYM and XMID and SCAL respectively. None of the QTL appeared on chromosome five. CONCLUSIONS:Repeated observations on individuals were affected by at least nine QTLs. For most QTL a precise location could be determined. The QTL for the inflection point (XMID) was difficult to pinpoint and might actually exist of two closely linked QTL on chromosome one.

关键词： Latent Variable Genetic Correlation bayesian algorithm Single SNPs bayesian Variable Selection

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bayesian Approach Based Comment Spam Defending Tool

Bayesian Approach Based Comment Spam Defending Tool

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3rd International Conference on Information Security and Assurance

作者： Dhinakaran, Beatrice Cynthia Nagamalai, Dhinaharan Lee, Jae-Kwang Hannam Univ Dept Comp Engn Daejeon South Korea Wireilla Net Solut Madras Tamil Nadu India

ISBN: (纸本)9783642026164

Spam messes up user's inbox, consumes network resources and spread worms and viruses. Spam is flooding of unsolicited,unwanted e mail. Spam in blogs is called blog spam or comment spam. It is done by posting comments or flooding spams to the services such as blogs, forums,news,email archives and guestbooks. Blog spams generally appears on guestbooks or comment pages where spammers fill a comment box with spam words. In addition to wasting user's time with unwanted comments, spam also consumes a lot of bandwith. In this paper, we propose a software. tool to prevent such blog spams by using bayesian algorithm based technique. It is derived from Bayes' Theorem. It gives an output which has a probability that any comment is spam, given that it has certain words in it. With using our past entries and a comment entry, this value is obtained and compared with a threshold value to find if it exceeds the threshold value or not. By using this cocept, we developed a software tool to block comment spam. The experimetal results shows that the bayesian based tool is working well. This paper has the major findings and their significance of blog spam filter.

关键词： bayesian algorithm spam comment spam blog spam

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Filtering Spam by Using Factors Hyperbolic Trees

Filtering Spam by Using Factors Hyperbolic Trees

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IEEE Global Telecommunications Conference (GLOBECOM 08)

作者： Hou, Hailong Chen, Yan Beyah, Raheem Zhang, Yan-Qing Georgia State Univ Dept Comp Sci Atlanta GA 30302 USA

ISBN: (纸本)9781424423248

Most of current anti-spam techniques, like the bayesian anti-spam algorithm, primarily use lexical matching for filtering unsolicited bulk E-mails (UBE) and unsolicited commercial E-mails (UCE). However, precision of spam filtering is usually low when the lexical matching algorithms are used in real dynamic environments. For example, an E-mail of refrigerator advertisements is useful for most families, but it is useless for Eskimos. The lexical matching anti-spam algorithms cannot distinguish such processed E-mails that are junk to most people but are useful for others. We propose a Factors Hyperbolic Tree (FHT) based algorithm that, unlike the lexical matching algorithms, handles spam filtering in a dynamic environment by considering various relevant factors. The new Ranked Term Frequency (RTF) algorithm is proposed to extract indicators from E-mails that are related to environmental factors. Type-1 and Type-2 fuzzy logic systems are used to evaluate the indicators and determine whether E-mails are spam based on the environmental factors. Additionally, weights of factors in a FHT database are continuously updated according to dynamic conditional factors in a real environment. Simulation results show that the FHT algorithm filters out spam with high precision. Furthermore, the FHT algorithm is more efficient than other methods when it filters E-mails with complex influencing factors. The main contribution of this paper is that the FHT based algorithm can filter E-mails based on influencing factors instead of matched words to allow dynamic filtering of spam E-mails.

关键词： spam bayesian algorithm Ranked Term Frequency fuzzy logic factors hyperbolic trees

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Utilization of K-NN algorithm for Expectation Maximization Based Classification Method

Utilization of K-NN Algorithm for Expectation Maximization B...

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4th International IEEE Conference Intelligent Systems

作者： Aci, M. Inan, C. Avci, M. Cukurova Univ Balcali Turkey

ISBN: (纸本)9781424417391

K Nearest Neighbor and bayesian algorithms are effective methods of machine learning. In this work a data elimination approach is proposed to improve data clustering. The proposed method is based on hybridization of K Nearest Neighbor and bayesian learning algorithms. The suggested method is tested on well-known machine learning data sets such as Iris, Wine and Breast Cancer and the results are concluded.

关键词： bayesian algorithm classification hybrid method k nearest neighbor

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A Markov chain Monte Carlo algorithm for bayesian dynamic signature verification

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IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2006年第1期1卷 22-34页

作者： Muramatsu, Daigo Kondo, Mitsuru Sasaki, Masahiro Tachibana, Satoshi Matsumoto, Takashi Waseda Univ Dept Elect Engn & Biosci Tokyo 1698555 Japan Univ Cambridge Dept Engn Signal Proc Grp Cambridge CB2 1PZ England

Authentication of handwritten signatures is becoming increasingly important. With a rapid increase in the number of people who access Tablet PCs and PDAs, online signature verification is one of the most promising techniques for signature verification. This paper proposes a new algorithm that performs a Monte Carlo based bayesian scheme for online signature verification. The new algorithm consists of a learning phase and a testing phase. In the learning phase, semi-parametric models are trained using the Markov Chain Monte Carlo (MCMC) technique to draw posterior samples of the parameters involved. In the testing phase, these samples are used to evaluate the probability that a signature is genuine. The proposed algorithm achieved an EER of 1.2% against the MCYT signature corpus where random forgeries are used for learning and skilled forgeries are used for evaluation. An experimental result is also reported with skilled forgery data for learning.

关键词： bayesian algorithm identification of persons Markov chain Monte Carlo signature trajectories

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Web page classification using modified naive bayesian approach

Web page classification using modified naive Bayesian approa...

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IEEE Region 10 Conference (TENCON 2006)

作者： Tomar, G. S. Verma, Shekhar Jha, Ashish Jaypee Inst Engn & Technol Guna 473226 India ABV Indian Inst Informat Technol & Management Gwalior India

ISBN: (纸本)9781424405480

This paper introduces the concept of a classification tool for web pages called WebClassify, which uses modified naive bayesian algorithm with multinomial model to classify pages into various categories. The tool starts the classification from downloading training web text from internet, preparing the hypertext for mining, and then storing web data in a local database. The paper also gives an account of choosing naive bayesian approach over other approaches for web text mining. The experimental results along with the classification accuracy analysis with increasing vocabulary size, is also shown.

关键词： bayesian algorithm competency ontology stemming

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Comparison of various methods for simultaneous retrieval of surface emissivities and gas properties in gray participating media

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JOURNAL OF HEAT TRANSFER-TRANSACTIONS OF THE ASME 2006年第8期128卷 829-837页

作者： Deiveegan, M. Balaji, C. Venkateshan, S. P. Indian Inst Technol Dept Mech Engn Heat Transfer & Thermal Power Lab Madras 600036 Tamil Nadu India

An inverse radiation analysis for simultaneous estimation of the radiative properties and the surface emissivities for a participating medium in between infinitely long parallel planes, from the knowledge of the measured temperatures and heat fluxes at the boundaries, is presented. The differential, discrete ordinate method is employed to solve the radiative transfer equation. The present analysis considers three types of simple scattering phase functions. The inverse problem is solved through minimization of a performance function, which is expressed by the sum of squares of residuals between calculated and observed temperatures and heat fluxes at the boundaries. To check the performance and accuracy in retrieval, a comparison is presented between four retrieval methods, viz. Levenberg-Marquardt algorithm, genetic. algorithm, artificial neural network, and the bayesian algorithm. The results of the present analyses indicate that good precision in retrieval could be achieved by using only temperatures and heat fluxes at,the boundaries. The study shows that. the radiative properties of medium and surface emissivities can be retrieved even with noisy data using bayesian retrieval algorithm and artificial neural network. Also, the results demonstrate that genetic algorithms are not efficient but are quite robust. Additionally, it is observed that an increase in the error in measurements significantly deteriorates the retrieval using the Levenberg-Marquardt algorithm.

关键词： inverse radiation problem parameter estimation discrete ordinate method Levenberg-Marquardt algorithm genetic algorithm artificial neural network bayesian algorithm

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An evaluation of recurrent neural network modelling for the prediction of damage evolution during forming

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JOURNAL OF MATERIALS PROCESSING TECHNOLOGY 2005年第3期170卷 551-562页

作者： Xiong, YS Withers, PJ Univ Manchester Manchester Mat Sci Ctr Manchester M1 7HS Lancs England

This paper examines the efficiency and capability of Dynet, a recurrent neural network model for the prediction of the damage evolution during hot non-uniform, non-isothermal forging on the basis of a limited number of damage snapshots during the process. A bayesian algorithm is introduced to optimise the hyperparameters related to the noise level and weight decay. In order to examine the capability of the model to capture the underlying trends when presented with sparse and noisy evidence, a synthetic relation between damage accumulation in a metal matrix composite and strain, strain rate and deformation temperature has been used to generate training data (evidence) of varying accuracy and sparseness. The results show that the bayesian algorithm performs very well, and that no significant overfitting is observed. In addition, this algorithm not only gives the expectation value of damage level, but also an estimate of its uncertainty. (c) 2005 Elsevier B.V. All rights reserved.

关键词： forging damage evolution microstructure modelling metal matrix cornposites recurrent neural network bayesian algorithm

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THE INTEGRATED DISTRIBUTED HYDROLOGICAL MODEL, ECOFLOW-A TOOL FOR CATCHMENT MANAGEMENT

THE INTEGRATED DISTRIBUTED HYDROLOGICAL MODEL, ECOFLOW-A TOO...

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作者： Nikolay Sokrut KTH Royal Institute of Technology

学位级别：博士

In order to find effective measures that meet the requirements for proper groundwater quality and quantity management, there is a need to develop a Decision Support System (DSS) and a suitable modelling tool. Central components of a DSS for groundwater management are thought to be models for surface- and groundwater flow and solute transport. The most feasible approach seems to be integration of available mathematical models, and development of a strategy for evaluation of the uncertainty propagation through these models. The physically distributed hydrological model ECOMAG has been integrated with the groundwater model MODFLOW to form a new integrated watershed modelling system - ECOFLOW. The modelling system ECOFLOW has been developed and embedded in Arc View. The multiple-scale modelling principle, combines a more detailed representation of the groundwater flow conditions with lumped watershed modelling, characterised by simplicity in model use, and a minimised number of model parameters. A bayesian statistical downscaling procedure has also been developed and implemented in the model. This algorithm implies downscaling of the parameters used in the model, and leads to decreasing of the uncertainty level in the modelling results. The integrated model ECOFLOW has been applied to the Vemmenhog catchment, in Southern Sweden, and the Orsundaan catchment, in central Sweden. The applications demonstrated that the model is capable of simulating, with reasonable accuracy, the hydrological processes within both the agriculturally dominated watershed (Vemmenhog) and the forest dominated catchment area (Orsundaan). The results show that the ECOFLOW model adequately predicts the stream and groundwater flow distribution in these watersheds, and that the model can be used as a possible tool for simulation of surface– and groundwater processes on both local and regional scales. A chemical module ECOMAG-N has been created and tested on the Vemmenhog watershed with a highly dense

关键词： Multiple-scale hydrological modelling bayesian algorithm Parameter estimation Groundwater management Statistical analysis

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bayesian and non-bayesian methods for iterative joint decoding and detection in the presence of phase noise

Bayesian and non-Bayesian methods for iterative joint decodi...

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IEEE International Symposium on Information Theory

作者： Colavolpe, G Barbieri, A Caire, G Bonneau, N Univ Parma Dipartimento Ingn Informaz I-43100 Parma Italy

ISBN: (纸本)0780382803

This paper proposes new algorithms for joint iterative decoding and parameter estimation in bayesian and nonbayesian method. The low-density parity-check (LDPC) codes in the presence of phase noise is focused in this paper. The nonbayesian algorithm for an unknown phase channel is based on the application of the expectation-maximization (EM) algorithm. The proposed bayesian algorithms are obtained as an application of the sum-product algorithm to the factor graph. Comparing the performance of the considered algorithms in the case of LDPC code, the EM-KL (EM-Karhunen-Loeve) algorithm performs better than EM-SW (sliding window) algorithm.

关键词： iterative decoding phase noise parameter estimation parity check codes optimisation graph theory Karhunen-Loeve transforms bayesian algorithm nonbayesian algorithm joint iterative decoding low-density parity-check code LDPC phase channel expectation-maximization Karhunen-Loeve algorithm EM algorithm sum-product algorithm factor graph

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