5 papers
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…
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…
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…
Regime Learning for Differentiable Particle Filters
John-Joseph Brady, Yuhui Luo, Wenwu Wang +2
Differentiable particle filters are an emerging class of models that combine sequential Monte Carlo techniques with the flexibility of neural networks to perform state space infere…
Revisiting semi-supervised training objectives for differentiable particle filters
Jiaxi Li, John-Joseph Brady, Xiongjie Chen +1
Differentiable particle filters combine the flexibility of neural networks with the probabilistic nature of sequential Monte Carlo methods. However, traditional approaches rely on…