# A new method for parameter estimation in probabilistic models: Minimum probability flow

arXiv: https://arxiv.org/abs/2007.09240 · HTML: https://mlsanity.com/paper/2007.09240/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jul 2020.

## Built on by (most cited first, 5 of 5)

- [How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Models](https://mlsanity.com/paper/2102.08921/) (2021)
- [From Denoising Diffusions to Denoising Markov Models](https://mlsanity.com/paper/2211.03595/) (2022)
- [Self-Supervised Discovery of Neural Circuits in Spatially Patterned Neural Responses with Graph Neural Networks](https://mlsanity.com/paper/2509.17174/) (2025)
- [Redundancy Maximization as a Principle of Associative Memory Learning in Hopfield Networks](https://mlsanity.com/paper/2511.02584/) (2025)
- [Implicit Bias and Invariance: How Hopfield Networks Efficiently Learn Graph Orbits](https://mlsanity.com/paper/2512.14338/) (2025)

## Abstract (arXiv)

Fitting probabilistic models to data is often difficult, due to the general intractability of the partition function. We propose a new parameter fitting method, Minimum Probability Flow (MPF), which is applicable to any parametric model. We demonstrate parameter estimation using MPF in two cases: a continuous state space model, and an Ising spin glass. In the latter case it outperforms current techniques by at least an order of magnitude in convergence time with lower error in the recovered coupling parameters.