Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: EnLLVM: Ensemble Based Nonlinear Bayesian Filtering Using Linear Latent
  Variable Models   Real-time nonlinear Bayesian filtering algorithms are overwhelmed by data
volume, velocity and increasing complexity of computational models. In this
paper, we propose a novel ensemble based nonlinear Bayesian filtering approach
which only requires a small number of simulations and can be applied to
high-dimensional systems in the presence of intractable likelihood functions.
The proposed approach uses linear latent projections to estimate the joint
probability distribution between states, parameters, and observables using a
mixture of Gaussian components generated by the reconstruction error for each
ensemble member. Since it leverages the computational machinery behind linear
latent variable models, it can achieve fast implementations without the need to
compute high-dimensional sample covariance matrices. The performance of the
proposed approach is compared with the performance of ensemble Kalman filter on
a high-dimensional Lorenz nonlinear dynamical system.

10
The categories of this paper are: q-bio
30
The categories of this paper are: q-alg
24
The categories of this paper are: dg-ga
9
The categories of this paper are: nlin
36
The categories of this paper are: cmp-lg
14
The categories of this paper are: math-ph
17
The categories of this paper are: q-fin
28
The categories of this paper are: alg-geom
23
The categories of this paper are: solv-int
0
The categories of this paper are: hep-ph
18
The categories of this paper are: econ
13
The categories of this paper are: quant-ph
31
The categories of this paper are: ao-sci
4
The categories of this paper are: cond-mat
19
The categories of this paper are: eess
8
The categories of this paper are: hep-ex