# Gradient Importance Sampling

arXiv: https://arxiv.org/abs/1507.05781 · HTML: https://mlsanity.com/paper/1507.05781/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jul 2015.

## Built on by (most cited first, 7 of 7)

- [A Survey of Monte Carlo Methods for Parameter Estimation](https://mlsanity.com/paper/2107.11820/) (2021)
- [Cooperation in the Latent Space: The Benefits of Adding Mixture Components in Variational Autoencoders](https://mlsanity.com/paper/2209.15514/) (2022)
- [Gradient-Guided Importance Sampling for Learning Binary Energy-Based Models](https://mlsanity.com/paper/2210.05782/) (2022)
- [Hamiltonian Adaptive Importance Sampling](https://mlsanity.com/paper/2209.13716/) (2022)
- [Efficient Bayes Inference in Neural Networks through Adaptive Importance Sampling](https://mlsanity.com/paper/2210.00993/) (2022)
- [Adaptive posterior distributions for uncertainty analysis of covariance matrices in Bayesian inversion problems for multioutput signals](https://mlsanity.com/paper/2501.01148/) (2025)
- [ISTRBoost: Importance Sampling Transfer Regression using Boosting](https://mlsanity.com/paper/2204.12044/) (2022)

## Abstract (arXiv)

Adaptive Monte Carlo schemes developed over the last years usually seek to ensure ergodicity of the sampling process in line with MCMC tradition. This poses constraints on what is possible in terms of adaptation. In the general case ergodicity can only be guaranteed if adaptation is diminished at a certain rate. Importance Sampling approaches offer a way to circumvent this limitation and design sampling algorithms that keep adapting. Here I present a gradient informed variant of SMC (and its special case Population Monte Carlo) for static problems.