Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: New approach to Bayesian high-dimensional linear regression   Consider the problem of estimating parameters $X^n \in \mathbb{R}^n $,
generated by a stationary process, from $m$ response variables $Y^m =
AX^n+Z^m$, under the assumption that the distribution of $X^n$ is known. This
is the most general version of the Bayesian linear regression problem. The lack
of computationally feasible algorithms that can employ generic prior
distributions and provide a good estimate of $X^n$ has limited the set of
distributions researchers use to model the data. In this paper, a new scheme
called Q-MAP is proposed. The new method has the following properties: (i) It
has similarities to the popular MAP estimation under the noiseless setting.
(ii) In the noiseless setting, it achieves the "asymptotically optimal
performance" when $X^n$ has independent and identically distributed components.
(iii) It scales favorably with the dimensions of the problem and therefore is
applicable to high-dimensional setups. (iv) The solution of the Q-MAP
optimization can be found via a proposed iterative algorithm which is provably
robust to the error (noise) in the response variables.

24
The categories of this paper are: dg-ga
21
The categories of this paper are: adap-org
31
The categories of this paper are: ao-sci
10
The categories of this paper are: q-bio
7
The categories of this paper are: hep-th
29
The categories of this paper are: funct-an
14
The categories of this paper are: math-ph
27
The categories of this paper are: comp-gas
0
The categories of this paper are: hep-ph
36
The categories of this paper are: cmp-lg
13
The categories of this paper are: quant-ph
25
The categories of this paper are: patt-sol
37
The categories of this paper are: supr-con
26
The categories of this paper are: bayes-an
11
The categories of this paper are: hep-lat
5
The categories of this paper are: gr-qc