We wish to contribute to the discussion of ‘Comparing Consensus Monte Carlo Strategies for Distributed Bayesian Computation’ by offering our views on the current best methods for Bayesian computation, both at big-data scale and with smaller data sets, as summarized in Table 1. This table is certainly an over-simplification of a highly complicated area of research in constant (present and likely future) flux, but we believe that constructing summaries of this type is worthwhile despite their drawbacks, if only to facilitate further discussion. Comment: A brief survey of the current state of play for Bayesian computation in data science at Big-Data scale

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