A Hilbert space embedding of a distribution—in short, a kernel mean embedding—has
recently emerged as a powerful tool for machine learning and statistical inference. The basic …

Earth observation through remote sensing images allows the accurate characterization and
identification of materials on the surface from space and airborne platforms. Multiple and …

Theoretical Foundations of Functional Data Analysis, with an Introduction to Linear
Operators provides a uniquely broad compendium of the key mathematical concepts and …

We study a class of dynamical systems modelled as stationary Markov chains that admit an
invariant distribution via the corresponding transfer or Koopman operator. While data-driven …

This book is an introduction to the field of asymptotic statistics. The treatment is both practical
and mathematically rigorous. In addition to most of the standard topics of an asymptotics …

A local density functional theory of the ground electronic states of atoms and molecules is
generated from three assumptions:(i) The energy functional is local.(ii) The chemical …

In principle, the exponential of a matrix could be computed in many ways. Methods involving
approximation theory, differential equations, the matrix eigenvalues, and the matrix …

In this book, Robert Wald provides a coherent, pedagogical introduction to the formulation of
quantum field theory in curved spacetime. He begins with a treatment of the ordinary one …

We prove that there exist infinite families of regular bipartite Ramanujan graphs of every
degree bigger than 2. We do this by proving a variant of a conjecture of Bilu and Linial about …

A Hilbert space embedding for probability measures has recently been proposed, with
applications including dimensionality reduction, homogeneity testing, and independence …