Mario Román

Evidential Decision Theory via Partial Markov Categories (Di Lavore, Román)

2 min read

Abstract

WePhoto introduce partial Markov categories. In the same way that Markov categories encode stochastic processes, partial Markov categories encode stochastic processes with constraints, observations and updates. In particular, we prove a synthetic Bayes theorem; we use it to define a syntactic partial theory of observations on any Markov category, whose normalisations can be computed in the original Markov category. Finally, we formalise Evidential Decision Theory in terms of partial Markov categories, and provide implemented examples.

@inproceedings{evidential23,
  author       = {Elena {Di Lavore} and
                  Mario Rom{\'{a}}n},
  title        = {Evidential Decision Theory via Partial Markov Categories},
  booktitle    = {38th Annual {ACM/IEEE} Symposium on Logic in Computer Science, {LICS}
                  2023, Boston, MA, USA, June 26-29, 2023},
  pages        = {1--14},
  publisher    = {{IEEE}},
  year         = {2023},
  url          = {https://doi.org/10.1109/LICS56636.2023.10175776},
  doi          = {10.1109/LICS56636.2023.10175776},
  timestamp    = {Wed, 29 May 2024 16:05:22 +0200},
}

Notes on the paper.

References.

Graph View

Backlinks