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Descripción
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| Dealing with the whole dataset in big data estimation problems is usually unfeasible. A common solution then consists of dividing the data into several smaller sets, performing distributed Bayesian estimation and combining these partial estimates to obtain a global estimate. A major problem of this approach is the presence of a non-negligible bias in the partial estimators due to the mismatch between the unknown true prior and the prior assumed in the estimation. A simple method to mitigate the effect of this bias is proposed in this paper. Essentially, the approach is based on using a reference data set to obtain a rough estimation of the parameter of interest, i.e., a reference parameter. This information is then communicated to the partial filters that handle the smaller data sets, which can thus use a refined prior centered around this parameter. Simulation results confirm the good performance of this scheme. | |
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Internacional
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Si |
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Nombre congreso
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IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) |
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Tipo de participación
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970 |
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Lugar del congreso
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Cancún (México) |
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Revisores
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Si |
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ISBN o ISSN
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978-1-4799-1963-5 |
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DOI
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Fecha inicio congreso
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13/12/2015 |
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Fecha fin congreso
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16/12/2015 |
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Desde la página
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257 |
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Hasta la página
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260 |
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Título de las actas
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Proceedings of the 6th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) |