Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A new avenue for Bayesian inference with INLA   Integrated Nested Laplace Approximations (INLA) has been a successful
approximate Bayesian inference framework since its proposal by Rue et al.
(2009). The increased computational efficiency and accuracy when compared with
sampling-based methods for Bayesian inference like MCMC methods, are some
contributors to its success. Ongoing research in the INLA methodology and
implementation thereof in the R package R-INLA, ensures continued relevance for
practitioners and improved performance and applicability of INLA. The era of
big data and some recent research developments, presents an opportunity to
reformulate some aspects of the classic INLA formulation, to achieve even
faster inference, improved numerical stability and scalability. The improvement
is especially noticeable for data-rich models. We demonstrate the efficiency
gains with various examples of data-rich models, like Cox's proportional
hazards model, an item-response theory model, a spatial model including
prediction, and a 3-dimensional model for fMRI data.

36
The categories of this paper are: cmp-lg
35
The categories of this paper are: mtrl-th
23
The categories of this paper are: solv-int
12
The categories of this paper are: nucl-th
33
The categories of this paper are: plasm-ph
25
The categories of this paper are: patt-sol
18
The categories of this paper are: econ
2
The categories of this paper are: math
21
The categories of this paper are: adap-org
14
The categories of this paper are: math-ph
10
The categories of this paper are: q-bio
4
The categories of this paper are: cond-mat
37
The categories of this paper are: supr-con
8
The categories of this paper are: hep-ex
19
The categories of this paper are: eess
34
The categories of this paper are: chem-ph