The paper proposes Metropolis adjusted Langevin and Hamiltonian Monte Carlo sampling
methods defined on the Riemann manifold to resolve the shortcomings of existing Monte …

Large scale models of physical phenomena demand the development of new statistical and
computational tools in order to be effective. Many such models are “sloppy,” ie, exhibit …

Parameter estimation by nonlinear least-squares minimization is a common problem that
has an elegant geometric interpretation: the possible parameter values of a model induce a …

This article summarizes recent work in optimal experimental design in nonlinear problems,
in which the major difficulty in obtaining good or optimal designs is their dependence on the …

The retrieval problems arising in atmospheric remote sensing belong to the class of the-
called discrete ill-posed problems. These problems are unstable under data perturbations …

Knowledge of how nonlinear a stochastic system is important for many applications. For
example, a full-blown nonlinear filter is needed in general if the system is highly nonlinear …

Nonlinearity, among other factors, is often the root cause of difficulties in nonlinear problems.
It is important to quantify a problem's degree of nonlinearity to decide a proper solution. For …

D-optimal experimental designs for precise estimation in nonlinear regression models are
obtained by minimizing the determinant of the approximate variance–covariance matrix of …

When modeling complex biological systems, exploring parameter space is critical, because
parameter values are typically poorly known a priori. This exploration can be challenging …

This book tackles the Optimal Non-Linear Experimental Design problem from an
applications perspective. At the same time it offers extensive mathematical background …