Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Improving the particle filter in high dimensions using conjugate
  artificial process noise   The particle filter is one of the most successful methods for state inference
and identification of general non-linear and non-Gaussian models. However,
standard particle filters suffer from degeneracy of the particle weights, in
particular for high-dimensional problems. We propose a method for improving the
performance of the particle filter for certain challenging state space models,
with implications for high-dimensional inference. First we approximate the
model by adding artificial process noise in an additional state update, then we
design a proposal that combines the standard and the locally optimal proposal.
This results in a bias-variance trade-off, where adding more noise reduces the
variance of the estimate but increases the model bias. The performance of the
proposed method is empirically evaluated on a linear-Gaussian state space model
and on the non-linear Lorenz'96 model. For both models we observe a significant
improvement in performance over the standard particle filter.

28
The categories of this paper are: alg-geom
37
The categories of this paper are: supr-con
36
The categories of this paper are: cmp-lg
5
The categories of this paper are: gr-qc
18
The categories of this paper are: econ
24
The categories of this paper are: dg-ga
8
The categories of this paper are: hep-ex
0
The categories of this paper are: hep-ph
7
The categories of this paper are: hep-th
35
The categories of this paper are: mtrl-th
29
The categories of this paper are: funct-an
19
The categories of this paper are: eess
11
The categories of this paper are: hep-lat
12
The categories of this paper are: nucl-th
22
The categories of this paper are: chao-dyn
30
The categories of this paper are: q-alg