# Generalized maximum entropy estimation

arXiv: https://arxiv.org/abs/1708.07311 · HTML: https://mlsanity.com/paper/1708.07311/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Aug 2017.

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

- [Robust Generalization despite Distribution Shift via Minimum Discriminating Information](https://mlsanity.com/paper/2106.04443/) (2021)

## Abstract (arXiv)

We consider the problem of estimating a probability distribution that maximizes the entropy while satisfying a finite number of moment constraints, possibly corrupted by noise. Based on duality of convex programming, we present a novel approximation scheme using a smoothed fast gradient method that is equipped with explicit bounds on the approximation error. We further demonstrate how the presented scheme can be used for approximating the chemical master equation through the zero-information moment closure method, and for an approximate dynamic programming approach in the context of constrained Markov decision processes with uncountable state and action spaces.