Core Concepts
Normalising flow-based differentiable particle filters outperform traditional methods in parameter estimation and tracking performance.
Abstract
The content discusses the application of normalising flow-based differentiable particle filters in a one-dimensional linear Gaussian state-space model. The experiment evaluates the performance of different methods based on parameter estimation error, tracking performance, ELBO, and effective sample size.
Experiment Setup:
Linear Gaussian state-space model with unknown parameters.
Training objective: ELBO approximation.
Comparison metrics: Parameter estimation error, tracking performance, ELBO, effective sample size.
Experimental Results:
AESMC-bootstrap converges fastest but has higher parameter estimation error.
NF-DPF shows faster convergence, high ELBO, and low parameter estimation error.
PFNet exhibits poor tracking performance despite high ELBO.
Stats
NF-DPF는 AESMC-bootstrap보다 빠르게 수렴하고 낮은 매개변수 추정 오차를 보여줍니다.
PFNet은 ELBO가 높지만 추적 성능이 낮습니다.
Quotes
"NF-DPF converges faster than the AESMC and the PFRNN has the highest ELBO and the lowest parameter estimation error."
"The AESMC-bootstrap converges the fastest but exhibits slightly larger parameter estimation error than other methods."