# Diagnosing Suboptimal Cotangent Disintegrations in Hamiltonian Monte Carlo

arXiv: https://arxiv.org/abs/1604.00695 · HTML: https://mlsanity.com/paper/1604.00695/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Apr 2016.

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

- [Graph Structure Learning with Interpretable Bayesian Neural Networks](https://mlsanity.com/paper/2406.14786/) (2024)

## Abstract (arXiv)

When properly tuned, Hamiltonian Monte Carlo scales to some of the most challenging high-dimensional problems at the frontiers of applied statistics, but when that tuning is suboptimal the performance leaves much to be desired. In this paper I show how suboptimal choices of one critical degree of freedom, the cotangent disintegration, manifest in readily observed diagnostics that facilitate the robust application of the algorithm.