In this paper, we investigate the mixture arc on generalized statistical manifolds. We ensure that the generalization of the mixture arc is well defined and we are able to provide a generalization of the open exponential arc and its properties. We consider the model of a φ -family of distributions to describe our general statistical model
The non-parametric version of Information Geometry has been developed in recent years. The first bas...
The non-parametric version of Information Geometry has been developed in recent years. The first bas...
The non-parametric version of Information Geometry has been developed in recent years. The first bas...
In this paper, we investigate the mixture arc on generalized statistical manifolds. We ensure that t...
Results on mixture and exponential connections by open arcs are revised and used to prove additional...
The collection of all strictly positive probability densities, which are equivalent to a mea- sure ...
Statistical manifolds are representations of smooth families of probability density functions (ie su...
Statistical manifolds are representations of smooth families of probability density functions that a...
Statistical manifolds are representations of smooth families of probability density functions that a...
Statistical manifolds are representations of smooth families of probability density functions that a...
[[abstract]]Let XU(1) < XU(2) < ... < XU(n) < ... be the sequence of the upper record values from a ...
This thesis will take a look at the roots of modern-day information geometry and some applications i...
This paper outlines recent work by the author on infinite-dimensional statistical manifolds, employi...
Let $(X, Cal X, mu)$ be a measure space, and let $Cal M(X,Cal X,mu)$ denote the set of the $mu$-almo...
Let $(X, Cal X, mu)$ be a measure space, and let $Cal M(X,Cal X,mu)$ denote the set of the $mu$-almo...
The non-parametric version of Information Geometry has been developed in recent years. The first bas...
The non-parametric version of Information Geometry has been developed in recent years. The first bas...
The non-parametric version of Information Geometry has been developed in recent years. The first bas...
In this paper, we investigate the mixture arc on generalized statistical manifolds. We ensure that t...
Results on mixture and exponential connections by open arcs are revised and used to prove additional...
The collection of all strictly positive probability densities, which are equivalent to a mea- sure ...
Statistical manifolds are representations of smooth families of probability density functions (ie su...
Statistical manifolds are representations of smooth families of probability density functions that a...
Statistical manifolds are representations of smooth families of probability density functions that a...
Statistical manifolds are representations of smooth families of probability density functions that a...
[[abstract]]Let XU(1) < XU(2) < ... < XU(n) < ... be the sequence of the upper record values from a ...
This thesis will take a look at the roots of modern-day information geometry and some applications i...
This paper outlines recent work by the author on infinite-dimensional statistical manifolds, employi...
Let $(X, Cal X, mu)$ be a measure space, and let $Cal M(X,Cal X,mu)$ denote the set of the $mu$-almo...
Let $(X, Cal X, mu)$ be a measure space, and let $Cal M(X,Cal X,mu)$ denote the set of the $mu$-almo...
The non-parametric version of Information Geometry has been developed in recent years. The first bas...
The non-parametric version of Information Geometry has been developed in recent years. The first bas...
The non-parametric version of Information Geometry has been developed in recent years. The first bas...
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