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Statistical Analysis of Network Data: Methods and Models
TLDR
In the past decade, the study of networks has increased dramatically. Expand
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Structural analysis of network traffic flows
TLDR
We present the first analysis of complete sets of OD flow time-series, taken from two different backbone networks (Abilene and Sprint-Europe). Expand
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Adaptive Bayesian Wavelet Shrinkage
Abstract When fitting wavelet based models, shrinkage of the empirical wavelet coefficients is an effective tool for denoising the data. This article outlines a Bayesian approach to shrinkage,Expand
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Statistical Analysis of Network Data
TLDR
In this short course, we will cover the foundations common to the statistical analysis of network data across the disciplines, from a statistical perspective, in the context of topics like network summary and visualization, network sampling, network modeling and inference, and network processes. Expand
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Emergent network topology at seizure onset in humans
Epilepsy - the world's most common serious brain disorder - is defined by recurrent unprovoked seizures that result from complex interactions between distributed neural populations. We explore someExpand
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Bayesian Multiscale Models for Poisson Processes
Abstract I introduce a class of Bayesian multiscale models (BMSM's) for one-dimensional inhomogeneous Poisson processes. The focus is on estimating the (discretized) intensity function underlying theExpand
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Coalescence and Fragmentation of Cortical Networks during Focal Seizures
Epileptic seizures reflect a pathological brain state characterized by specific clinical and electrical manifestations. The proposed mechanisms are heterogeneous but united by the supposition thatExpand
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Graph wavelets for spatial traffic analysis
TLDR
A number of problems in network operations and engineering call for new methods of traffic analysis based on the wavelet transform. Expand
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WAVELET SHRINKAGE ESTIMATION OF CERTAIN POISSON INTENSITY SIGNALS USING CORRECTED THRESHOLDS
Wavelet shrinkage estimation has been found to be a powerful tool for the non-parametric estimation of spatially variable phenomena. Most work in this area to date has concentrated primarily on theExpand
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Multiscale likelihood analysis and complexity penalized estimation
We describe here a framework for a certain class of multiscale likelihood factorizations wherein, in analogy to a wavelet decomposition of an L 2 function, a given likelihood function has anExpand
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