Statistical model-based methods for observation selection in wireless sensor networks and for feature selection in classification

Title from PDF of title page (University of Missouri--Columbia, viewed on August 30, 2012).The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file.Dissertation advisor: Dr. Yi ShangIncludes bibliographical references.Vita.Ph. D. University of Missouri--Columbia 2012."May 2012"We apply statistical model-based approaches to address temporal and spatial observation selection challenges in wireless sensor networks. For temporal observation selection, we present an improved version of VoIDP algorithm that is the first optimal algorithms for efficiently selecting the subset of observations on chain graphical models. We validate the improvement in sensor scheduling experiments. For location-based observation selection, we address the challenge of placing vehicle detection sensors designed to optimize traffic signal controls by employing two greedy heuristics, entropy and submodular mutual information, based on Gaussian process models. We demonstrate their performance in a simulated traffic road networking map. Experimental results reveal insights of the two heuristics. We also compare the model-based approaches for sensor observation selection, and our experimental results show that the graphical model-based approach is more robust and error-tolerant than the Gaussian process model-based approach. Finally We also apply the submodular mutual information-based selection method to feature selection for classification problems. We compare the proposed method with existing state-of-the-art attribute selection methods through extensive experiments, and show that the proposed mutual information-based method perform comparably with, or even better than, other feature selection methods.Includes bibliographical reference

Active Learning for Undirected Graphical Model Selection

This paper studies graphical model selection, i.e., the problem of estimating a graph of statistical relationships among a collection of random variables. Conventional graphical model selection algorithms are passive, i.e., they require all the measurements to have been collected before processing begins. We propose an active learning algorithm that uses junction tree representations to adapt future measurements based on the information gathered from prior measurements. We prove that, under certain conditions, our active learning algorithm requires fewer scalar measurements than any passive algorithm to reliably estimate a graph. A range of numerical results validate our theory and demonstrates the benefits of active learning.Comment: AISTATS 201

Refining a Bayesian network using a chain event graph

The search for a useful explanatory model based on a Bayesian Network (BN) now has a long and successful history. However, when the dependence structure between the variables of the problem is asymmetric then this cannot be captured by the BN. The Chain Event Graph (CEG) provides a richer class of models which incorporates these types of dependence structures as well as retaining the property that conclusions can be easily read back to the client. We demonstrate on a real health study how the CEG leads us to promising higher scoring models and further enables us to make more refined conclusions than can be made from the BN. Further we show how these graphs can express causal hypotheses about possible interventions that could be enforced

Analyze This! A Cosmological Constraint Package for CMBEASY

We introduce a Markov Chain Monte Carlo simulation and data analysis package that extends the CMBEASY software. We have taken special care in implementing an adaptive step algorithm for the Markov Chain Monte Carlo in order to improve convergence. Data analysis routines are provided which allow to test models of the Universe against measurements of the cosmic microwave background, supernovae Ia and large scale structure. We present constraints on cosmological parameters derived from these measurements for a $\Lambda$CDM cosmology and discuss the impact of the different observational data sets on the parameters. The package is publicly available as part of the CMBEASY software at www.cmbeasy.org.Comment: Published version, JCAP style, 16 pages, 7 figures. The software is available at http://www.cmbeasy.or