Probabilistic programs are a powerful and convenient approach to formalising distributions over system executions. A classical verification problem for probabilistic programs is temporal inference : to compute the likelihood that the execution traces satisfy a given temporal property. This paper presents a general framework for temporal inference, which applies to a rich variety of quantitative models including those that arise in the operational semantics of probabilistic and weighted programs. The key idea underlying our framework is that in a variety of existing approaches, the main construction that enables temporal inference is that of a product between the system of interest and the temporal property. We provide a unifying mathematical definition of product constructions, enabled by the realisation that 1) both systems and temporal properties can be modelled as coalgebras and 2) product constructions are distributive laws in this context. Our categorical framework leads us to our main contribution: a sufficient condition for correctness, which is precisely what enables to use the product construction for temporal inference. We show that our framework can be instantiated to naturally recover a number of disparate approaches from the literature including, e.g., partial expected rewards in Markov reward models, resource-sensitive reachability analysis, and weighted optimization problems. Furthermore, we demonstrate a product of weighted programs and weighted temporal properties as a new instance to show the scalability of our approach.

@article{paperbot3190,
  title = {A Unifying Approach to Product Constructions for Quantitative Temporal Inference},
  author = {Kazuki Watanabe and Sebastian Junges and Jurriaan Rot and Ichiro Hasuo},
  journal = {Proceedings of the ACM on Programming Languages},
  volume = {9},
  number = {OOPSLA1},
  year = {2025},
  doi = {10.1145/3720501}
}