Approximately Sufficient Statistics and Bayesian Computation

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Abstract
The analysis of high-dimensional data sets is often forced to rely upon well-chosen summary statistics. A systematic approach to choosing such statistics, which is based upon a sound theoretical framework, is currently lacking. In this paper we develop a sequential scheme for scoring statistics according to whether their inclusion in the analysis will substantially improve the quality of inference. Our method can be applied to high-dimensional data sets for which exact likelihood equations are not possible. We illustrate the potential of our approach with a series of examples drawn from genetics. In summary, in a context in which well-chosen summary statistics are of high importance, we attempt to put the 'well' into 'chosen.'

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