Network Estimation Graphical Model - 1222 Words

3 Network estimation: graphical model The following projects involve network estimation problems encountered in different biological appli- cations such as gene-gene or protein-protein interaction. The main focus has been on to develop robust, scalable network estimation methodology. Quantile based graph estimation Graphical models are ubiquitous tools to describe the interdependence between variables measured si- multaneously such as large-scale gene or protein expression data. Gaussian graphical models (GGMs) are well-established tools for probabilistic exploration of dependence structures using precision matrices and they are generated under a multivariate normal joint distribution. However, they suffer from several shortcomings since†¦show more content†¦Stochastic approximation (SA) provides a fast recursive way for numerically maximizing a function under measurement error. Using suitably chosen weight/step-size the stochastic approximation algorithm converges to the true solution, which can be adapted to estimate the components of the mixing distribution from a mixture, in the form of recursively learning, predictive recursion method. The convergence depends on a martingale construction and convergence of related series and heavily depends on the independence. The general algorit hm may not hold if dependence is present. We have proposed a novel martingale decomposition to address the case of dependent data. 5 Measurement error model: small area estimation We proposed [4] a novel shrinkage type estimator and derived the optimum value of the shrinkage pa- rameter. The asymptotic value of the shrinkage coefficient depends on the Wasserstein metric between standardized distribution of the observed variable and the variable of interest. In the process, we also estab- lished the necessary and sufficient conditions for a recent conjecture about the shrinkage coefficient to hold. The biggest advantage of the proposed approach is that it is completely distribution free. This makes the estimators extremely robust and I also showed that the estimator continues to perform well with respect to the ‘best’ estimator derivedShow MoreRelatedResearch Statement : Texas A M University1438 Words   |  6 PagesResearch Statement Nilabja Guha Texas AM University My current research at Texas AM University is in a broad area of uncertainty quantification (UQ), with applications to inverse problems, transport based filtering, graphical models and online learning. My research projects are motivated by many real-world problems in engineering and life sciences. I have collaborated with researchers in engineering and bio-sciences on developing rigorous uncertainty quantification methods within Bayesian frameworkRead MoreOnline Learning : Stochastic Approximation1139 Words   |  5 Pagesrelated series and heavily depends on the independence of the data. The general algorithm may not hold if dependence is present. We have proposed a novel martingale decomposition to address the case of dependent data. 5 Measurement error model: small area estimation We proposed [4] a novel shrinkage type estimator and derived the optimum value of the shrinkage pa- rameter. The asymptotic value of the shrinkage coefficient depends on the Wasserstein metric between standardized distribution of the observedRead MoreEstimating The Mixing Density Of A Mixture Distribution951 Words   |  4 Pagesrelated series and heavily depends on the independence of the data. The general algorithm may not hold if dependence is present. We have proposed a novel martingale decomposition to address the case of dependent data. 5 Measurement error model: small area estimation We proposed [4] a novel shrinkage type estimator and derived the optimum value of the shrinkage pa- rameter. The asymptotic value of the shrinkage coefficient depends on the Wasserstein metric between standardized distribution of the observedRead MoreThe Static Model Of Data Mining Essay1710 Words   |  7 PagesAbstract: Lot of research done in mining software repository. In this paper we discussed about the static model of data mining to extract defect .Different algorithms are used to find defects like naà ¯ve bayes algorithm, Neural Network, Decision tree. But Naà ¯ve Bayes algorithm has the best result .Data mining approach are used to predict defects in software .We used NASA dataset namely, Data rive. Software metrics are also used to find defects. 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(WBS) * Review historical information and compare to current activities. * Include a contingency buffer for potential risks. * Solicit advice from others that have previously completed similar activities. * Identify and documentRead MoreNetwork Security And Situational Awareness Data Pre Processing Method Based On Conditional Random Fields1418 Words   |  6 PagesNetwork Security and Situational Awareness Data Pre-processing Method Based on Conditional Random Fields Rajesh.P #1, Krishnamoorthy.P #2 Gopi.S#3 ,Sivasankari.S #4 Assistant Professor CSE*1,2, Assistant Professor IT #3 #4, Kingston Engineering College*1,2,3 4 Vellore, India*1,2,3 4 1rajeshpcse@kingston.ac.in 2 krishnancse0206@gmail.com 3gopi.scse@gmail.com 4sivasankari_cse@yahoo.co.in Abstract The examination of NetworkRead MoreThe Cloud Of Cloud Computing2307 Words   |  10 Pagesattacks, using extensible resources and other cloud’s characteristics. 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SDLC, Software Development Life CycleRead MoreGenotypying Case Study1259 Words   |  6 Pages(Illumina, Inc.) and SNPs with minor allele frequency (MAF) of 0.05 were ruled out from further analysis. Subsequently 42,041 SNPs with minor allele frequency ≠¥ 0.05 across 491 genotypes were used for GWAS analysis. Linkage disequilibrium estimation Glyma.Wm.82.a2 reference genome will be used to obtain chromosome physical lengths (bp) through SoyBase (Grant, Nelson, et al., 2010) to calculate genome-wide inter marker distance and chromosome-wide densities. Linkage disequilibrium (LD) between