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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)
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