AI Article Synopsis
- The study introduces a methodology for sequential inference in nonlinear stochastic state-space models, focusing on estimating both dynamic states and fixed parameters simultaneously.
- It highlights the limitations of basic particle filters due to parameter estimation issues and proposes using kernel density approximation to improve parameter regeneration.
- Additionally, it discusses how the choice of priors impacts posterior inferences and recommends using more constrained priors, while also advocating for better resampling techniques in particle filters, illustrated through a disease outbreak tracking model.
Article Abstract
We present general methodology for sequential inference in nonlinear stochastic state-space models to simultaneously estimate dynamic states and fixed parameters. We show that basic particle filters may fail due to degeneracy in fixed parameter estimation and suggest the use of a kernel density approximation to the filtered distribution of the fixed parameters to allow the fixed parameters to regenerate. In addition, we show that "seemingly" uninformative uniform priors on fixed parameters can affect posterior inferences and suggest the use of priors bounded only by the support of the parameter. We show the negative impact of using multinomial resampling and suggest the use of either stratified or residual resampling within the particle filter. As a motivating example, we use a model for tracking and prediction of a disease outbreak via a syndromic surveillance system. Finally, we use this improved particle filtering methodology to relax prior assumptions on model parameters yet still provide reasonable estimates for model parameters and disease states.
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| http://dx.doi.org/10.1016/j.mbs.2014.06.018 | DOI Listing |
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