Practical Filtering with Sequential Parameter Learning
نویسندگان
چکیده
This paper develops a simulation-based approach to sequential parameter learning and filtering in general state-space models. Our methodology is based on a rolling-window Markov chain Monte Carlo (MCMC) approach and can be easily implemented by modifying state-space smoothing algorithms. Furthermore, the filter avoids the degeneracies that hinder particle filters and is robust to outliers. We illustrate the methodology on a benchmark autoregressive where we show the robustness to outliers. In a three-dimensional nonlinear stochastic Lorenz model, we exploit a conditionally Gaussian structure for the states to provide a fast algorithm for simultaneously performing sequential parameter learning and filtering.
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