3 papers
cs.LG2026
Efficient Learning of Deep State Space Models via Importance Smoothing
John-Joseph Brady, Nikolas Nusken, Yunpeng Li
Latent state space systems are ubiquitous in statistical modelling, arising naturally when time series are observed through noisy measurements. However, training deep state space m…
eess.SP2025
PyDPF: A Python Package for Differentiable Particle Filtering
John-Joseph Brady, Benjamin Cox, Yunpeng Li +1
State-space models (SSMs) are a widely used tool in time series analysis. In the complex systems that arise from real-world data, it is common to employ particle filtering (PF), an…
stat.ML2024
Differentiable Interacting Multiple Model Particle Filtering
John-Joseph Brady, Yuhui Luo, Wenwu Wang +2
We propose a sequential Monte Carlo algorithm for parameter learning when the studied model exhibits random discontinuous jumps in behaviour. To facilitate the learning of high dim…