We consider the problem of learning a Riemannian metric associated with a given differentiable manifold and a set of points. Our approach to the problem involves choosing a metric from a parametric family that is based on maximizing the inverse volume of a given dataset of points. From a statistical perspective, it is related to maximum likelihood under a model that assigns probabilities invers...

We present in this paper a novel non-parametric approach useful for clustering Markov processes. We introduce a pre-processing step consisting in mapping multivariate independent and identically distributed samples from random variables to a generic non-parametric representation which factorizes dependency and marginal distribution apart without losing any. An associated metric is defined where...

This paper extends a previously proposed algorithm for generating unstructured meshes in three-dimensional and in twodimensional domains to generate surface meshes. A surface mesh is generated in parametric space and mapped to Cartesian space. Finite elements may be stretched on parametric space, but they present a good-quality shape on the 3D surface. The algorithm uses a metric map defined by...

This paper extends a previously proposed algorithm for generating unstructured meshes in three-dimensional and in twodimensional domains to generate surface meshes. A surface mesh is generated in parametric space and mapped to Cartesian space. Finite elements may be stretched on parametric space, but they present a good-quality shape on the 3D surface. The algorithm uses a metric map defined by...

In Model Based Development (MBD) of embedded systems, it is often desirable to not only verify/falsify certain formal system specifications, but also to automatically explore the properties that the system satisfies. Namely, given a parametric specification, we would like to automatically infer the ranges of parameters for which the property holds/does not hold on the system. In this paper, we ...

Motivated by recent developments on calculus in metric measure spaces (X, d,m), we prove a general duality principle between Fuglede’s notion [15] of p-modulus for families of finite Borel measures in (X, d) and probability measures with barycenter in Lq(X,m), with q dual exponent of p ∈ (1,∞). We apply this general duality principle to study null sets for families of parametric and non-paramet...

We develop a family of infinite-dimensional (i.e. non-parametric) manifolds of probability measures. The latter are defined on underlying Banach spaces, and have densities of class Ck b with respect to appropriate reference measures. The case k = ∞, in which the manifolds are modelled on Fréchet spaces, is included. The manifolds admit the Fisher-Rao metric and the dually flat geometry of Amari...

We propose a new class of algorithms for minimizing or maximizing functions of parametric probabilistic models. These new algorithms are natural gradient algorithms that leverage more information than prior methods by using a new metric tensor in place of the commonly used Fisher information matrix. This new metric tensor is derived by computing directions of steepest ascent where the distance ...

MOTIVATION In analyses of microarray data with a design of different biological conditions, ranking genes by their differential 'importance' is often desired so that biologists can focus research on a small subset of genes that are most likely related to the experiment conditions. Permutation methods are often recommended and used, in place of their parametric counterparts, due to the small sam...