We describe the design, semantics, and implementation of a probabilistic programming language where programs are spreadsheet queries. Given an input database consisting of tables held in a spreadsheet, a query constructs a probabilistic model conditioned by the spreadsheet data, and returns an output database determined by inference. This work extends probabilistic programming systems in three ...

*Correspondence: Fabrizio Riguzzi , Dipartimento di Matematica e Informatica, Università di Ferrara, Via Saragat 1, Ferrara 44122, Italy e-mail: [email protected] The field of Probabilistic Logic Programming (PLP) has seen significant advances in the last 20 years, with many proposals for languages that combine probability with logic programming. Since the start, the problem of learning...

Probabilistic programming languages allow a modeler to build probabilistic models using complex data structures with all the power of a programming language. We present CTPPL, an expressive probabilistic programming language for dynamic processes that models processes using continuous time. Time is a first class element in our language; the amount of time taken by a subprocess can be specified ...

Answer Set Programming (ASP) is a powerful declarative computing paradigm that is especially suitable for modeling commonsense reasoning problems. However, the crisp nature of the underlying semantics, the stable model semantics, makes it difficult to handle reasoning domains involving probability and inconsistency. To address this issue, we present an extension of logic programs under the stab...

Irrigation water management is crucial for agricultural production and livelihood security in many regions and countries throughout the world. Over the past decades, controversial and conflictladen water-allocation issues among competing municipal, industrial and agricultural interests have raised increasing concerns. Particularly, growing population, varying natural conditions and shrinking wa...

Fleischhacker et al., (2022). Generative DAGs as an Interface Into Probabilistic Programming with the R Package causact. Journal of Open Source Software, 7(76), 4415, https://doi.org/10.21105/joss.04415

Probabilistic techniques are central to data analysis, but different approaches can be challenging to apply, combine, and compare. This paper introduces composable generative population models (CGPMs), a computational abstraction that extends directed graphical models and can be used to describe and compose a broad class of probabilistic data analysis techniques. Examples include discriminative...