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Adding a systematic uncertainty to the signal estimation in the on/off-zone measurements
Adding a systematic uncertainty the signal estimation in the on/off-zone measurements
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2013/4/28
The measurements with the background estimation from an off-zone are widely used in astrophysics, accelerator physics and other areas. Usually, the expected number of the background events in the off-...
Node-Based Learning of Multiple Gaussian Graphical Models
graphical models structured sparsity alternating direction method of multipliers gene regulatory networks lasso multivariate normal
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2013/4/28
We consider the problem of estimating high-dimensional Gaussian graphical models corresponding to a single set of variables under several distinct conditions. This problem is motivated by the task of ...
Bayesian Networks Aplied to Therapy Monitoring
Bayesian Networks Aplied Therapy Monitoring
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2013/5/2
We propose a general Bayesian network model for application in a wide class of problems of therapy monitoring. We discuss the use of stochastic simulation as a computational approach to inference on t...
Compressive Shift Retrieval
Compressed sensing shift retrieval sig-nal reconstruction signal registration
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2013/5/2
The classical shift retrieval problem considers two signals in vector form that are related by a cyclic shift. In this paper, we develop a compressive variant where the measurement of the signals is u...
The RAppArmor Package: Enforcing Security Policies in R Using Dynamic Sandboxing on Linux
R Security Linux Sandbox AppArmor
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2013/5/2
With the increasing availability of public cloud computing facilities and scientific super computers, there is a great potential for making R available through public or shared resources. This allows ...
Performance of the stochastic MV-PURE estimator in highly noisy settings
robust linear estimation reduced-rank estimation stochastic MV-PURE estimator array signal processing
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2013/4/28
The stochastic MV-PURE estimator has been developed to provide linear estimation robust to ill-conditioning, high noise levels, and imperfections in model knowledge. In this paper, we investigate the ...
Object Oriented Data Analysis of Cell-Well Structured Data
data objects cell con uence bright
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2013/4/28
Object oriented data analysis (OODA) aims at statistically analyzing populations of complicated objects. This paper is motivated by a study of cell images in cell culture biology, which highlights a c...
On the sphericity test with large-dimensional observations
Large-dimensional data Sphericity test John’stest CLT for linear spectral statistics Large-dimensional sample covariance matrix
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2013/4/27
In this paper, we propose corrections to LRT and John's test for sphericity in large-dimension. New formula for the limiting parameters in the CLT for linear spectral statistics of sample covariance m...
Regression with Distance Matrices
functional data analysis mixed data multidimensional scaling shape correlation ma-trix
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2013/4/27
Data types that lie in metric spaces but not in vector spaces are difficult to use within the usual regression setting, either as the response and/or a predictor. We represent the information in these...
Topic Discovery through Data Dependent and Random Projections
Topic Discovery through Data Dependent and Random Projections
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2013/4/27
We present algorithms for topic modeling based on the geometry of cross-document word-frequency patterns. This perspective gains significance under the so called separability condition. This is a cond...
Variational Semi-blind Sparse Deconvolution with Orthogonal Kernel Bases and its Application to MRFM
Variational Bayesian inference posterior image distribution image reconstruction hyperparameter estimation MRFM experiment
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2013/5/2
We present a variational Bayesian method of joint image reconstruction and point spread function (PSF) estimation when the PSF of the imaging device is only partially known. To solve this semi-blind d...
A multiple filter test for change point detection in renewal processes with varying variance
A multiple filter test change point detection renewal processes varying variance
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2013/4/27
Non-stationarity of the event rate is a persistent problem in modeling time series of events, such as neuronal spike trains. Motivated by a variety of patterns in neurophysiological spike train record...
We define and study a generalization of Sobol sensitivity indices for the case of a vector output.
A dependent partition-valued process for multitask clustering and time evolving network modelling
A dependent partition-valued process multitask clustering time evolving network modelling
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2013/4/27
The fundamental aim of clustering algorithms is to partition data points. We consider tasks where the discovered partition is allowed to vary with some covariate such as space or time. One approach wo...
Group-Sparse Model Selection: Hardness and Relaxations
Signal Approximation Structured Sparsity Interpretability Tractability Dynamic Programming Compressive Sensing
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2013/5/2
Group-based sparsity models are proven instrumental in linear regression problems for recovering signals from much fewer measurements than standard compressive sensing. The main promise of these model...