16 citations · 16 across the 4 of their papers we have counts for
4 papers
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…
Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks
Benjamin Cox, Santiago Segarra, Victor Elvira
State-space models are a popular statistical framework for analysing sequential data. Within this framework, particle filters are often used to perform inference on non-linear stat…
GraphGrad: Efficient Estimation of Sparse Polynomial Representations for General State-Space Models
Benjamin Cox, Emilie Chouzenoux, Victor Elvira
State-space models (SSMs) are a powerful statistical tool for modelling time-varying systems via a latent state. In these models, the latent state is never directly observed. Inste…
Sparse Bayesian Estimation of Parameters in Linear-Gaussian State-Space Models
Benjamin Cox, Victor Elvira
State-space models (SSMs) are a powerful statistical tool for modelling time-varying systems via a latent state. In these models, the latent state is never directly observed. Inste…