Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Amortised inference of fractional Brownian motion with linear
  computational complexity   We introduce a simulation-based, amortised Bayesian inference scheme to infer
the parameters of random walks. Our approach learns the posterior distribution
of the walks' parameters with a likelihood-free method. In the first step a
graph neural network is trained on simulated data to learn optimized
low-dimensional summary statistics of the random walk. In the second step an
invertible neural network generates the posterior distribution of the
parameters from the learnt summary statistics using variational inference. We
apply our method to infer the parameters of the fractional Brownian motion
model from single trajectories. The computational complexity of the amortized
inference procedure scales linearly with trajectory length, and its precision
scales similarly to the Cram{\'e}r-Rao bound over a wide range of lengths. The
approach is robust to positional noise, and generalizes well to trajectories
longer than those seen during training. Finally, we adapt this scheme to show
that a finite decorrelation time in the environment can furthermore be inferred
from individual trajectories.

17
The categories of this paper are: q-fin
9
The categories of this paper are: nlin
27
The categories of this paper are: comp-gas
28
The categories of this paper are: alg-geom
15
The categories of this paper are: nucl-ex
4
The categories of this paper are: cond-mat
2
The categories of this paper are: math
22
The categories of this paper are: chao-dyn
6
The categories of this paper are: astro-ph
34
The categories of this paper are: chem-ph
29
The categories of this paper are: funct-an
12
The categories of this paper are: nucl-th
21
The categories of this paper are: adap-org
32
The categories of this paper are: atom-ph
11
The categories of this paper are: hep-lat
35
The categories of this paper are: mtrl-th