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cs.LG2024
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…
stat.CO2024
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…