The possibilities for positioning in cellular networks has increased over time, pushed by increased needs for location based products and services for a variety of purposes. It all started with rou ...

/pdf/on-timing-based-localization-in-cellular-radio-networks-193ya2shde.pdf

On Timing-Based Localization in Cellular Radio Networks

The letter studies the problem of sequential estima- tion of mixtures in diffusion networks whose nodes communicate only with their adjacent neighbors. The adopted quasi-Bayesian approach yields a probabilistically consistent and computationally non-intensiveandfastmethod,applicabletoawideclassofmixture models with unknown component parameters and weights. More- over, if conjugate priors are used for inferring the component pa- rameters, the solution attains a closed analytic form. Index Terms—Diffusion, distributed parameter estimation, sensor networks, sequential mixture estimation.

/pdf/sequential-estimation-of-mixtures-in-diffusion-networks-218os4ribp.pdf

Sequential Estimation of Mixtures in Diffusion Networks

The material in this chapter is largely tutorial in nature. The main goal is to review two types of deterministic approximation, variational approximations and the expectation propagation approach, which have been developed mainly in the computer science literature, but with some statistical antecedents, to assist approximate Bayesian inference. However, we believe that it is helpful to preface discussion of these methods with an elementary reminder of the EM algorithm as a way of computing posterior modes. All three approaches have now been applied to many model types, but we shall just mention them in the context of mixtures, and only a very small number of types of mixture at that.

The EM Algorithm, Variational Approximations and Expectation Propagation for Mixtures

The Bayes solution to the unsupervised sequential learning problem induced by a mixture model for the two-class signal versus noise decision problem generates a computational and storage explosion. A quasi-Bayes approximate learning procedure is proposed that avoids the computational explosion while retaining the flavor of the Bayes solution. Convergence is established and efficiency is investigated.

Unsupervised learning for signal versus noise (Corresp.)

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Unsupervised learning for signal versus noise (Corresp.)