Differentiable Particle Filtering using Optimal Placement Resampling
Domonkos Csuzdi Olivér Törő Tamás Bécsi
Particle filters are a frequent choice for inference tasks in nonlinear and
non-Gaussian state-space models. They can either be used for state inference by
approximating the filtering distribution or for parameter inference by
approximating the marginal data (observation) likelihood. A good proposal
distribution and a good resampling scheme are crucial to obtain low variance
estimates. However, traditional methods like multinomial resampling introduce
nondifferentiability in PF-based loss functions for parameter estimation,
prohibiting gradient-based learning tasks. This work proposes a differentiable
resampling scheme by deterministic sampling from an empirical cumulative
distribution function. We evaluate our method on parameter inference tasks and
proposal learning.