Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Anomaly Detection and Localisation using Mixed Graphical Models   We propose a method that performs anomaly detection and localisation within
heterogeneous data using a pairwise undirected mixed graphical model. The data
are a mixture of categorical and quantitative variables, and the model is
learned over a dataset that is supposed not to contain any anomaly. We then use
the model over temporal data, potentially a data stream, using a version of the
two-sided CUSUM algorithm. The proposed decision statistic is based on a
conditional likelihood ratio computed for each variable given the others. Our
results show that this function allows to detect anomalies variable by
variable, and thus to localise the variables involved in the anomalies more
precisely than univariate methods based on simple marginals.

7
The categories of this paper are: hep-th
5
The categories of this paper are: gr-qc
34
The categories of this paper are: chem-ph
1
The categories of this paper are: cs
22
The categories of this paper are: chao-dyn
23
The categories of this paper are: solv-int
28
The categories of this paper are: alg-geom
25
The categories of this paper are: patt-sol
18
The categories of this paper are: econ
11
The categories of this paper are: hep-lat
3
The categories of this paper are: physics
33
The categories of this paper are: plasm-ph
24
The categories of this paper are: dg-ga
27
The categories of this paper are: comp-gas
21
The categories of this paper are: adap-org
6
The categories of this paper are: astro-ph