The intrinsic dimensionality of an inverse problem is affected by prior
information, the accuracy and number of observations, and the smoothing
properties of the forward operator. From a Bayesian perspective, changes from
the prior to the posterior may, in many problems, be confined to a relatively
low-dimensional subspace of the parameter space. We present a dimension
reduction approach that defines and identifies such a subspace, called the
"likelihood-informed subspace" (LIS), by characterizing the relative influences
of the prior and the likelihood over the support of the posterior distribution.
This identification enables new and more efficient computational methods for
Bayesian inference with nonlinear forward models and Gaussian priors. In
particular, we approximate the posterior distribution as the product of a
lower-dimensional posterior defined on the LIS and the prior distribution
marginalized onto the complementary subspace. Markov chain Monte Carlo sampling
can then proceed in lower dimensions, with significant gains in computational
efficiency. We also introduce a Rao-Blackwellization strategy that
de-randomizes Monte Carlo estimates of posterior expectations for additional
variance reduction. We demonstrate the efficiency of our methods using two
numerical examples: inference of permeability in a groundwater system governed
by an elliptic PDE, and an atmospheric remote sensing problem based on Global
Ozone Monitoring System (GOMOS) observations
