Turing.jl Newsletter 7
MCMCChains@7
There’s a new major version of MCMCChains.jl. From a user point of view, the main difference is that summary statistics and quantiles aren’t automatically calculated by default (so, printing a Chains object in a REPL will only show the parameter names and sizes). To get the summary statistics and quantiles you will have to run describe(chain). The main reason for this is because the summary stats would often take quite a while to compute — if you wish to preserve the old behaviour you can stick to MCMCChains@6.
JuliaBUGS @model
JuliaBUGS.jl recently implemented a @model macro which, in terms of its syntax, looks somewhat similar to Turing.jl’s own macro, but under the hood constructs a BUGS model. Perhaps of interest is the way that parameters are initialised using @parameters struct ... end; this currently helps to initialise all parameter values to placeholders, and offers an alternative to explicitly specifying this with NamedTuples or arrays. This hasn’t yet been released but for more information and a demonstration, there’s a documentation page that describes the design of this macro. The existing @bugs macro will still be retained.
Libtask
Libtask.jl, the library that Turing’s particle Gibbs sampler is built on, was recently rewritten for its core parts by Will Tebbutt (#179. Libtask implements copyable, resumable tasks (coroutines) in pure Julia, and the new version is much faster and better documented. The new implementation is based on source code transformations, using tools and techniques from Mooncake.jl. Work is ongoing to adapt AdvancedPS.jl to work with the new Libtask version, and once that is done we should expect a performance boost for Turing’s particle Gibbs sampler. We’ll let you know once that’s out.
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