Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Bayesian inference with information content model check for Langevin
  equations   The Bayesian data analysis framework has been proven to be a systematic and
effective method of parameter inference and model selection for stochastic
processes. In this work we introduce an information content model check which
may serve as a goodness-of-fit, like the chi-square procedure, to complement
conventional Bayesian analysis. We demonstrate this extended Bayesian framework
on a system of Langevin equations, where coordinate dependent mobilities and
measurement noise hinder the normal mean squared displacement approach.

22
The categories of this paper are: chao-dyn
27
The categories of this paper are: comp-gas
19
The categories of this paper are: eess
17
The categories of this paper are: q-fin
31
The categories of this paper are: ao-sci
12
The categories of this paper are: nucl-th
35
The categories of this paper are: mtrl-th
2
The categories of this paper are: math
24
The categories of this paper are: dg-ga
28
The categories of this paper are: alg-geom
33
The categories of this paper are: plasm-ph
10
The categories of this paper are: q-bio
14
The categories of this paper are: math-ph
15
The categories of this paper are: nucl-ex
6
The categories of this paper are: astro-ph
13
The categories of this paper are: quant-ph