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

Divergence measures for statistical data processing—An annotated bibliography -
ScienceDirect Skip to main contentSkip to article Elsevier logo Journals & Books Search …

Information geometry is the differential geometric treatment of statistical models. It thereby
provides the mathematical foundation of statistics. Information geometry therefore is of …

This is the first comprehensive book on information geometry, written by the founder of the
field. It begins with an elementary introduction to dualistic geometry and proceeds to a wide …

Information divergences are commonly used to measure the dissimilarity of two elements on
a statistical manifold. Differentiable manifolds endowed with different divergences may …

In this paper, we extend and overview wide families of Alpha-, Beta-and Gamma-
divergences and discuss their fundamental properties. In literature usually only one single …

To quantify user–item preferences, a recommender system (RS) commonly adopts a high-
dimensional and sparse (HiDS) matrix. Such a matrix can be represented by a non-negative …

Geometric Modeling in Probability and Statistics Page 1 Ovidiu Calin · Constantin Udrişte
Geometric Modeling in Probability and Statistics Page 2 Geometric Modeling in Probability and …

We propose a class of multiplicative algorithms for Nonnegative Matrix Factorization (NMF)
which are robust with respect to noise and outliers. To achieve this, we formulate a new …

We study losses for binary classification and class probability estimation and extend the
understanding of them from margin losses to general composite losses which are the …