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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
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
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
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...
Linear system identification using stable spline kernels and PLQ penalties
linear system identification bias-variance trade off kernel-based regularization robust statistics interior point methods piecewise linear quadratic densities
2013/4/27
The classical approach to linear system identification is given by parametric Prediction Error Methods (PEM). In this context, model complexity is often unknown so that a model order selection step is...
Convergence rate of Markov chain methods for genomic motif discovery
Gibbs sampler DNA slow mixing spectral gap multimodal
2013/4/27
We analyze the convergence rate of a simplified version of a popular Gibbs sampling method used for statistical discovery of gene regulatory binding motifs in DNA sequences. This sampler satisfies a v...
Recovering Non-negative and Combined Sparse Representations
underdetermined linear system sparse representations non-negative constraints orthogonal matching pursuit unique sparse solution
2013/5/2
The non-negative solution to an underdetermined linear system can be uniquely recovered sometimes, even without imposing any additional sparsity constraints. In this paper, we derive conditions under ...
Automated Bayesian System Identification with NARX Models
Automated Bayesian System Identification NARX Models
2013/5/2
We introduce GP-FNARX: a new model for nonlinear system identification based on a nonlinear autoregressive exogenous model (NARX) with filtered regressors (F) where the nonlinear regression problem is...
Gaussian Processes for Nonlinear Signal Processing
Gaussian Processes Nonlinear Signal Processing
2013/5/2
Gaussian processes (GPs) are versatile tools that have been successfully employed to solve nonlinear estimation problems in machine learning, but that are rarely used in signal processing. In this tut...
Machine Learning for Bioclimatic Modelling
Machine Learning Bioclimatic Modelling Geographic Range Artificial Neural Network Evolutionary Algorithm
2013/5/2
Many machine learning (ML) approaches are widely used to generate bioclimatic models for prediction of geographic range of organism as a function of climate. Applications such as prediction of range s...
Classification of Segments in PolSAR Imagery by Minimum Stochastic Distances Between Wishart Distributions
Region-Based Classification Stochastic Distances Hypothesis Tests Polarimetry Wishart distribution
2013/4/28
A new classifier for Polarimetric SAR (PolSAR) images is proposed and assessed in this paper. Its input consists of segments, and each one is assigned the class which minimizes a stochastic distance. ...
State estimation under non-Gaussian Levy noise: A modified Kalman filtering method
Kalman filter modified Kalman filter Non-Gaussiannoise L′evy noise state estimation data assimilation
2013/4/28
The Kalman filter is extensively used for state estimation for linear systems under Gaussian noise. When non-Gaussian L\'evy noise is present, the conventional Kalman filter may fail to be effective d...
On the Performance Limits of Map-Aware Localization
Localization Cramer-Rao bound Ziv-Zikai bound Weiss-Weinstein Bound A Priori Information Map
2013/4/28
Establishing bounds on the accuracy achievable by localization techniques represents a fundamental technical issue. Bounds on localization accuracy have been derived for cases in which the position of...
Capturing Patterns via Parsimonious t Mixture Models
Factor analysis Facial representation Image compression PGMM PTMM
2013/4/27
his paper exploits a simplified version of the mixture of multivariate t-factor analyzers (MtFA) for robust mixture modelling and clustering of high-dimensional data that frequently contain a number o...
Possible Directions for Improving Dependency Versioning in R
Possible Directions Improving Dependency Versioning R
2013/4/28
One of the most powerful features of R is its infrastructure for contributed code. The built-in package manager and complementary repositories provide a great system for development and exchange of co...