Comment: A brief survey of the current state of play for Bayesian computation in data science at Big-Data scale

TL;DR

This paper provides a brief survey of current Bayesian computation methods at big-data and smaller scales, aiming to summarize and facilitate discussion in a rapidly evolving research area.

Contribution

It offers a summarized overview of the state of Bayesian computation methods, highlighting current best practices and fostering further discussion.

Findings

Summarizes key Bayesian computation strategies for big data.

Highlights the complexity and ongoing evolution of the field.

Provides a simplified comparison to aid understanding and discussion.

Abstract

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.