# Iterated Block Particle Filter for High-dimensional Parameter Learning: Beating the Curse of Dimensionality

arXiv: https://arxiv.org/abs/2110.10745 · HTML: https://mlsanity.com/paper/2110.10745/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 44 references that are in the map.
- First posted on arXiv 20 Oct 2021.

## Builds on (most cited first, 3 of 3)

- [Ensemble Kalman methods for high-dimensional hierarchical dynamic space-time models](https://mlsanity.com/paper/1704.06988/) (2017)
- [Inference on high-dimensional implicit dynamic models using a guided intermediate resampling filter](https://mlsanity.com/paper/1708.08543/) (2017)
- [Spatiotemporal blocking of the bouncy particle sampler for efficient inference in state space models](https://mlsanity.com/paper/2101.03079/) (2021)

## Built on by (most cited first, 2 of 2)

- [Transformers Can Solve Non-Linear and Non-Markovian Filtering Problems in Continuous Time For Conditionally Gaussian Signals](https://mlsanity.com/paper/2310.19603/) (2023)
- [Robust Iterative Learning Hidden Quantum Markov Models](https://mlsanity.com/paper/2510.23237/) (2025)

## Abstract (arXiv)

Parameter learning for high-dimensional, partially observed, and nonlinear stochastic processes is a methodological challenge. Spatiotemporal disease transmission systems provide examples of such processes giving rise to open inference problems. We propose the iterated block particle filter (IBPF) algorithm for learning high-dimensional parameters over graphical state space models with general state spaces, measures, transition densities and graph structure. Theoretical performance guarantees are obtained on beating the curse of dimensionality (COD), algorithm convergence, and likelihood maximization. Experiments on a highly nonlinear and non-Gaussian spatiotemporal model for measles transmission reveal that the iterated ensemble Kalman filter algorithm (Li et al. (2020)) is ineffective and the iterated filtering algorithm (Ionides et al. (2015)) suffers from the COD, while our IBPF algorithm beats COD consistently across various experiments with different metrics.