Section on Bayesian Computation
Over the past twenty years, Bayesian computation has been a tremendous catalyst in Bayesian ideas reaching practitioners – statisticians and non-statisticians alike. It has also provided a fantastic arena for original research in algorithmic statistics and numerical probability, not to mention other fields at the interface. At this more mature stage of its development, at a time when ambitions of statisticians and the expectations on statistics grow, Bayesian computation must remain a major area of research and innovation. If it does, then principled methods of statistical analysis can continue to be both readily available and customarily implemented, as we deal with data on a (much) larger scale, in higher dimensions and with more complex structure.
We invite all members with (any degree of) interest in computation for Bayesian inference to join the newly created ISBA Section on Bayesian Computation (BayesComp) – and that means both researchers involved in developing new computational methods and associated theory, and users of Bayesian statistical methods interested in implementing, sharing, disseminating, or learning best practice. The purposes of the Section are as multifaceted as the aspects of Bayesian computation, including promoting original research into computational methods for inference and decision making, encouraging the use of frontier computational tools among practitioners, the development of adapted software, languages, platforms, and dedicated machines, and translating and disseminating among statisticians methods developed in other disciplines.
To address these purposes, the Section will – among other activities – organise conferences, workshops, short courses, webinars, and sessions in other meetings like ISBA and JSM, and will develop and maintain a website of information, tools, and advice as an authoritative central resource for Bayesian computation.