# Decision Making Using Probabilistic Inference Methods

arXiv: https://arxiv.org/abs/1303.5428 · HTML: https://mlsanity.com/paper/1303.5428/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Mar 2013.

## Built on by (most cited first, 1 of 1)

- [Detailed Balanced Chemical Reaction Networks as Generalized Boltzmann Machines](https://mlsanity.com/paper/2205.06313/) (2022)

## Abstract (arXiv)

The analysis of decision making under uncertainty is closely related to the analysis of probabilistic inference. Indeed, much of the research into efficient methods for probabilistic inference in expert systems has been motivated by the fundamental normative arguments of decision theory. In this paper we show how the developments underlying those efficient methods can be applied immediately to decision problems. In addition to general approaches which need know nothing about the actual probabilistic inference method, we suggest some simple modifications to the clustering family of algorithms in order to efficiently incorporate decision making capabilities.