Divide-and-conquer and statistical inference for big data

  title={Divide-and-conquer and statistical inference for big data},
  author={Michael I. Jordan},
I present some recent work on statistical inference for Big Data. Divide-and-conquer is a natural computational paradigm for approaching Big Data problems, particularly given recent developments in distributed and parallel computing, but some interesting challenges arise when applying divide-and-conquer algorithms to statistical inference problems. One interesting issue is that of obtaining confidence intervals in massive datasets. The bootstrap principle suggests resampling data to obtain… CONTINUE READING