Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Posterior mean and variance approximation for regression and time series
  problems   This paper develops a methodology for approximating the posterior first two
moments of the posterior distribution in Bayesian inference. Partially
specified probability models, which are defined only by specifying means and
variances, are constructed based upon second-order conditional independence, in
order to facilitate posterior updating and prediction of required
distributional quantities. Such models are formulated particularly for
multivariate regression and time series analysis with unknown observational
variance-covariance components. The similarities and differences of these
models with the Bayes linear approach are established. Several subclasses of
important models, including regression and time series models with errors
following multivariate $t$, inverted multivariate $t$ and Wishart
distributions, are discussed in detail. Two numerical examples consisting of
simulated data and of US investment and change in inventory data illustrate the
proposed methodology.

17
The categories of this paper are: q-fin
32
The categories of this paper are: atom-ph
14
The categories of this paper are: math-ph
9
The categories of this paper are: nlin
31
The categories of this paper are: ao-sci
0
The categories of this paper are: hep-ph
1
The categories of this paper are: cs
34
The categories of this paper are: chem-ph
25
The categories of this paper are: patt-sol
27
The categories of this paper are: comp-gas
33
The categories of this paper are: plasm-ph
22
The categories of this paper are: chao-dyn
3
The categories of this paper are: physics
11
The categories of this paper are: hep-lat
7
The categories of this paper are: hep-th
6
The categories of this paper are: astro-ph