Irina Brinster

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Learning conditional probability tables of large Bayesian Networks (BNs) with hidden nodes using the Expectation Maximization algorithm is heavily computationally intensive. There are at least two bottlenecks, namely the potentially huge data set size and the requirement for computation and memory resources. This work applies the distributed computing(More)
This work applies the distributed computing framework MapReduce to Bayesian network parameter learning from incomplete data. We formulate the classical Expectation Maximization (EM) algorithm within the MapReduce framework. Analytically and experimentally we analyze the speed-up that can be obtained by means of MapReduce. We present details of the MapReduce(More)
This demo presents PANDAA, a zero-configuration automatic spatial localization technique for networked devices based on ambient sound sensing. We will demonstrate that after initial placement of the devices, ambient sounds, such as human speech, music, footsteps, finger snaps, hand claps, or coughs and sneezes, can be used to autonomously resolve the(More)
An intention of MapReduce Sets for Summarization expressions analysis has to suggest criteria how summarization expressions in summarization data can be defined in a meaningful way and how they should be compared. Similitude based MapReduce Sets for summarization Expression Analysis and MapReduce Sets for Assignment is expected to adhere to fundamental(More)
Modern mobile devices incorporate several transmit and receive antennas in highly constrained volumes. As miniaturized antennas impinge upon fundamental physical limits on efficiency, new design approaches are required to support ever-smaller devices with more varied and robust communication performance. We take an unconventional design approach in which an(More)
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