Distributed Estimation, Information Loss and Exponential Families

  title={Distributed Estimation, Information Loss and Exponential Families},
  author={Qiang Liu and Alexander T. Ihler},
Distributed learning of probabilistic models from multiple data repositories with minimum communication is increasingly important. We study a simple communication-efficient learning framework that first calculates the local maximum likelihood estimates (MLE) based on the data subsets, and then combines the local MLEs to achieve the best possible approximation to the global MLE given the whole dataset. We study this framework’s statistical properties, showing that the efficiency loss compared to… CONTINUE READING