2 citations · 2 across the 5 of their papers we have counts for
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…
Differentiable Bootstrap Particle Filters for Regime-Switching Models
Wenhan Li, Xiongjie Chen, Wenwu Wang +2
Differentiable particle filters are an emerging class of particle filtering methods that use neural networks to construct and learn parametric state-space models. In real-world app…