Bayesian learning for hidden Markov model with Gaussian mixture state observation densities
Speech Communication - Eurospeech '91
Learning to learn
M&M: multi-level Markov model for wireless link simulations
Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems
On credibility of simulation studies of telecommunication networks
IEEE Communications Magazine
Enabling building energy auditing using adapted occupancy models
Proceedings of the Third ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings
Improving wireless link simulation using multilevel markov models
ACM Transactions on Sensor Networks (TOSN)
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The mixture of multivariate Bernoulli distributions (MMB) is a statistical model for high-dimensional binary data in widespread use. Recently, the MMB has been used to model the sequence of packet receptions and losses of wireless links in sensor networks. Given an MMB trained on long data traces recorded from links of a deployed network, one can then use samples from the MMB to test different routing algorithms for as long as desired. However, learning an accurate model for a new link requires collecting from it long traces over periods of hours, a costly process in practice (e.g. limited battery life). We propose an algorithm that can adapt a preexisting MMB trained with extensive data to a new link from which very limited data is available. Our approach constrains the new MMB's parameters through a nonlinear transformation of the existing MMB's parameters. The transformation has a small number of parameters that are estimated using a generalized EM algorithm with an inner loop of BFGS iterations. We demonstrate the efficacy of the approach using the MNIST dataset of handwritten digits, and wireless link data from a sensor network. We show we can learn accurate models from data traces of about 1 minute, about 10 times shorter than needed if training an MMB from scratch.