One Step Impulse Radio Ultra-WideBand Positioning Algorithm
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In this technical report, we propose a methodology to use the communication network infra- structure, in particular WiFi traces, to detect the sequence of activity episodes visited by pedestrians. Due to the poor quality of WiFi localization, a probabilist ...
We examine the robustness and privacy properties of Bayesian inference under assumptions on the prior, but without any modifications to the Bayesian framework. First, we generalise the concept of differential privacy to arbitrary dataset distances, outcome ...
Composite likelihoods are increasingly used in applications where the full likelihood is analytically unknown or computationally prohibitive. Although some frequentist properties of the maximum composite likelihood estimator are akin to those of the maximu ...
Academia Sinica, Institute of Statistical Science2012
We study the effect of deployment of low cost, low power micro base stations along with macro base stations on energy consumption and capacity of downlink LTE. [1] studied this problem, using spectral area efficiency as the performance metric. We show that ...
Acm Order Department, P O Box 64145, Baltimore, Md 21264 Usa2011
We report on maximum entropy method study of the experimental atomic and ionic charges of LiBD4 in its low-temperature orthorhombic phase. Synchrotron radiation x-ray powder diffraction data, neutron powder diffraction data, and density functional calculat ...
This paper investigates the use of phoneme class conditional probabilities as features (posterior features) for template-based ASR. Using 75 words and 600 words task-independent and speaker-independent setup on Phonebook database, we investigate the use of ...
When system identification methodologies are used to interpret measurement data taken from structures, uncertainty dependencies are in many cases unknown due to model simplifications and omissions. This paper presents how error-domain model falsification r ...
Model specification is an integral part of any statistical inference problem. Several model selection techniques have been developed in order to determine which model is the best one among a list of possible candidates. Another way to deal with this questi ...
We present a class of models that, via a simple construction, enables exact, incremental, non-parametric, polynomial-time, Bayesian inference of conditional measures. The approach relies upon creating a sequence of covers on the conditioning variable and m ...
This paper addresses the problem of state estimation in the case where the prior distribution of the states is not perfectly known but instead is parameterized by some unknown parameter. Thus in order to support the state estimator with prior information o ...