  In this paper, we improve the PAC-Bayesian error bound for linear regression
derived in Germain et al. [10]. The improvements are twofold. First, the
proposed error bound is tighter, and converges to the generalization loss with
a well-chosen temperature parameter. Second, the error bound also holds for
training data that are not independently sampled. In particular, the error
bound applies to certain time series generated by well-known classes of
dynamical models, such as ARX models.
