# Incorporating the ChEES Criterion into Sequential Monte Carlo Samplers

arXiv: https://arxiv.org/abs/2504.02627 · HTML: https://mlsanity.com/paper/2504.02627/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 40 references that are in the map.
- First posted on arXiv 3 Apr 2025.

## Builds on (most cited first, 9 of 9)

- [A Conceptual Introduction to Hamiltonian Monte Carlo](https://mlsanity.com/paper/1701.02434/) (2017)
- [Composable Effects for Flexible and Accelerated Probabilistic Programming in NumPyro](https://mlsanity.com/paper/1912.11554/) (2019)
- [TensorFlow Distributions](https://mlsanity.com/paper/1711.10604/) (2017)
- [tfp.mcmc: Modern Markov Chain Monte Carlo Tools Built for Modern Hardware](https://mlsanity.com/paper/2002.01184/) (2020)
- [Automatically Batching Control-Intensive Programs for Modern Accelerators](https://mlsanity.com/paper/1910.11141/) (2019)
- [Enhanced SMC2: Leveraging Gradient Information from Differentiable Particle Filters Within Langevin Proposals](https://mlsanity.com/paper/2407.17296/) (2024)
- [SIMD Parallel MCMC Sampling with Applications for Big-Data Bayesian Analytics](https://mlsanity.com/paper/1310.1537/) (2013)
- [Robust Particle Density Tempering for State Space Models](https://mlsanity.com/paper/1805.00649/) (2018)
- [A Single SMC Sampler on MPI that Outperforms a Single MCMC Sampler](https://mlsanity.com/paper/1905.10252/) (2019)

## Built on by (most cited first, 3 of 3)

- [Hess-MC2: Sequential Monte Carlo Squared using Hessian Information and Second Order Proposals](https://mlsanity.com/paper/2507.07461/) (2025)
- [Utilising Gradient-Based Proposals Within Sequential Monte Carlo Samplers for Training of Partial Bayesian Neural Networks](https://mlsanity.com/paper/2505.03797/) (2025)
- [Investigating Batch Inference in a Sequential Monte Carlo Framework for Neural Networks](https://mlsanity.com/paper/2601.21983/) (2026)

## Abstract (arXiv)

Markov chain Monte Carlo (MCMC) methods are a powerful but computationally expensive way of performing non-parametric Bayesian inference. MCMC proposals which utilise gradients, such as Hamiltonian Monte Carlo (HMC), can better explore the parameter space of interest if the additional hyper-parameters are chosen well. The No-U-Turn Sampler (NUTS) is a variant of HMC which is extremely effective at selecting these hyper-parameters but is slow to run and is not suited to GPU architectures. An alternative to NUTS, Change in the Estimator of the Expected Square HMC (ChEES-HMC) was shown not only to run faster than NUTS on GPU but also sample from posteriors more efficiently. Sequential Monte Carlo (SMC) samplers are another sampling method which instead output weighted samples from the posterior. They are very amenable to parallelisation and therefore being run on GPUs while having additional flexibility in their choice of proposal over MCMC. We incorporate (ChEEs-HMC) as a proposal into SMC samplers and demonstrate competitive but faster performance than NUTS on a number of tasks.