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Consider a Bayesian inference problem where a variable of interest does nottake values in a Euclidean space. These "non-standard" data structures are inreality fairly common. They are frequently used in problems involving latentdiscrete factor models, networks, and domain specific problems such as sequencealignments and reconstructions, pedigrees, and phylogenies. In principle,Bayesian inference should be particularly well-suited in such scenarios, as theBayesian paradigm provides a principled way to obtain confidence assessment forrandom variables of any type. However, much of the recent work on makingBayesian analysis more accessible and computationally efficient has focused oninference in Euclidean spaces.  In this paper, we introduce Blang, a domain specific language and libraryaimed at bridging this gap. Blang allows users to perform Bayesian analysis onarbitrary data types while using a declarative syntax similar to BUGS. Blang isaugmented with intuitive language additions to create data types of the user'schoosing. To perform inference at scale on such arbitrary state spaces, Blangleverages recent advances in sequential Monte Carlo and non-reversible Markovchain Monte Carlo methods.

Blang: Bayesian declarative modelling of general data structures and inference via algorithms based on distribution continua