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Estimating Spatial Autocorrelation with Sampled Network Data
NetworkDataAnalysis Paired Maximum Likelihood Estimator
2016/1/26
Spatial autocorrelation is a parameter of importance for network data analysis. To estimate spatial autocorrelation, maximum likelihood has been popularly used. However, its rigorous implementation re...
Compressive Network Analysis
network data analysis compressive sensing Radon basis pursuit restricted isometry property clique detection
2016/1/25
Modern data acquisition routinely produces massive amounts of network data.Though many methods and models have been proposed to analyze such data, the research of network data is largely disconnected ...
Graph cluster randomization: network exposure to multiple universes
Graph cluster randomization network exposure multiple universes
2013/6/17
A/B testing is a standard approach for evaluating the effect of online experiments; the goal is to estimate the `average treatment effect' of a new feature or condition by exposing a sample of the ove...
Estimating Network Degree Distributions Under Sampling: An Inverse Problem, with Applications to Monitoring Social Media Networks
Estimating Network Degree Distributions Sampling An Inverse Problem Applications Monitoring Social Media Networks
2013/6/14
Networks are a popular tool for representing elements in a system and their interconnectedness. Many observed networks can be viewed as only samples of some true underlying network. Such is frequently...
Quantum Annealing for Dirichlet Process Mixture Models with Applications to Network Clustering
Quantum annealing Dirichlet process Stochastic optimization Maximum a posteriori estimation Bayesian nonparametrics
2013/6/17
We developed a new quantum annealing (QA) algorithm for Dirichlet process mixture (DPM) models based on the Chinese restaurant process (CRP). QA is a parallelized extension of simulated annealing (SA)...
Joint likelihood calculation for intervention and observational data from a Gaussian Bayesian network
Gaussian Bayesian networks causal effects intervention data Fisher information
2013/6/13
Methodological development for the inference of gene regulatory networks from transcriptomic data is an active and important research area. Several approaches have been proposed to infer relationships...
Latent variable models are frequently used to identify structure in dichotomous network
data, in part because they give rise to a Bernoulli product likelihood that is both well un-
derstood and cons...
Maximum Likelihood Estimation in Network Models
beta model polytope of degree sequences random graphs Rasch model p1 model
2011/6/20
We study maximum likelihood estimation for the statistical model for both directed and undirected
random graph models in which the degree sequences areminimal sufficient statistics. In the undirected...
Vine copulas as a mean for the construction of high dimensional probability distribution associated to a Markov Network
Copula decomposition t-cherry junction tree Markov network Cherry-wine probability distribution Graphical models
2011/6/17
Building higher-dimensional copulas is generally recognized as a difficult
problem. Regular-vines using bivariate copulas provide a flexible class of high-dimensional
dependency models. In large dim...
Optimal experiment design in a filtering context with application to sampled network data
Optimal design, Kalman filter random walks
2010/10/19
We examine the problem of optimal design in the context of filtering multiple random walks. Specifically, we define the steady state E-optimal design criterion and show that the underlying optimizatio...
Missing Data：A Comparison of Neural Network and Expectation Maximisation Techniques
Missing Data Neural Network Expectation Maximisation Techniques
2010/4/28
Two techniques have emerged from the recent literature as candidate solutions to the problem
of missing data imputation, and these are the Expectation Maximisation (EM) Algorithm and the
auto-associ...
In France, for administrative reasons, unemployed workers may actually be involved in occasional work while remaining identified as unemployed (and receiving the corresponding benefit). This is due to...
This set of lecture notes is a much expanded version of lecture notes developed and used by
the first author in courses at Stanford University from 1981 to 1984 and more recently beginning in 2002. T...
Target Detection via Network Filtering
Sparse network Lasso regression network topology target detection
2010/3/18
A method of ‘network filtering’ has been proposed recently to detect the effects of certain external perturbations on the interacting members in a network. However, with large networks, the goal of de...
Classifying Network Data with Deep Kernel Machines
deep architecture diffusion kernel kernel density estimation nearest centroid socialnetwork support vector machine
2010/3/9
Inspired by a growing interest in analyzing network data, we study the problem of node classifi-
cation on graphs, focusing on approaches based on kernel machines. Conventionally, kernel machines
ar...