Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Stochastic Formulation of Causal Digital Twin: Kalman Filter Algorithm   We provide some basic and sensible definitions of different types of digital
twins and recommendations on when and how to use them. Following up on our
recent publication of the Learning Causal Digital Twin, this article reports on
a stochastic formulation and solution of the problem. Structural Vector
Autoregressive Model (SVAR) for Causal estimation is recast as a state-space
model. Kalman filter (and smoother) is then employed to estimate causal factors
in a system of connected machine bearings. The previous neural network
algorithm and Kalman Smoother produced very similar results; however, Kalman
Filter/Smoother may show better performance for noisy data from industrial IoT
sources.

36
The categories of this paper are: cmp-lg
18
The categories of this paper are: econ
16
The categories of this paper are: stat
10
The categories of this paper are: q-bio
35
The categories of this paper are: mtrl-th
28
The categories of this paper are: alg-geom
5
The categories of this paper are: gr-qc
7
The categories of this paper are: hep-th
15
The categories of this paper are: nucl-ex
9
The categories of this paper are: nlin
25
The categories of this paper are: patt-sol
26
The categories of this paper are: bayes-an
3
The categories of this paper are: physics
37
The categories of this paper are: supr-con
12
The categories of this paper are: nucl-th
0
The categories of this paper are: hep-ph