Asymmetric information distances are used to define asymmetric norms and quasimetrics on the statistical manifold and its dual space of random variables. Quasimetric topology, generated by the Kullback-Leibler (KL) divergence, is considered as the main example, and some of its topological properties are investigated
This thesis will take a look at the roots of modern-day information geometry and some applications i...
We construct an infinite-dimensional Hilbert manifold of probability measures on an abstract measura...
AbstractThe condition for the curvature of a statistical manifold to admit a kind of standard hypers...
A systematic introduction to a general nonparametric theory of statistics on manifolds, with emphasi...
A new mathematical object called a preferred point geometry is introduced in order to (a) provide a ...
This thesis presents certain recent methodologies and some new results for the statistical analysis ...
A new mathematical object called a preferred point geometry is introduced in order to (a) provide a ...
In this paper, we present a review of recent developments on the κ -deformed statistical m...
Statistical manifolds are representations of smooth families of probability density functions (ie su...
Divergence functions are the non-symmetric “distance” on the manifold, Μθ, of parametric probability...
We explore the information geometric structure of the statistical manifold generated by the (kappa)-...
Measures of divergence between two points play a key role in many engineering problems. One such mea...
AbstractIn this paper we lift fundamental topological structures on probability measures and random ...
Abstract. The condition for the curvature of a statistical man-ifold to admit a kind of standard hyp...
Inferring and comparing complex, multivariable probability density functions is fundamental to probl...
This thesis will take a look at the roots of modern-day information geometry and some applications i...
We construct an infinite-dimensional Hilbert manifold of probability measures on an abstract measura...
AbstractThe condition for the curvature of a statistical manifold to admit a kind of standard hypers...
A systematic introduction to a general nonparametric theory of statistics on manifolds, with emphasi...
A new mathematical object called a preferred point geometry is introduced in order to (a) provide a ...
This thesis presents certain recent methodologies and some new results for the statistical analysis ...
A new mathematical object called a preferred point geometry is introduced in order to (a) provide a ...
In this paper, we present a review of recent developments on the κ -deformed statistical m...
Statistical manifolds are representations of smooth families of probability density functions (ie su...
Divergence functions are the non-symmetric “distance” on the manifold, Μθ, of parametric probability...
We explore the information geometric structure of the statistical manifold generated by the (kappa)-...
Measures of divergence between two points play a key role in many engineering problems. One such mea...
AbstractIn this paper we lift fundamental topological structures on probability measures and random ...
Abstract. The condition for the curvature of a statistical man-ifold to admit a kind of standard hyp...
Inferring and comparing complex, multivariable probability density functions is fundamental to probl...
This thesis will take a look at the roots of modern-day information geometry and some applications i...
We construct an infinite-dimensional Hilbert manifold of probability measures on an abstract measura...
AbstractThe condition for the curvature of a statistical manifold to admit a kind of standard hypers...
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