We investigate quantization coefficients for probability measures μ on limit sets, which are generated by systems S of infinitely many contractive similarities and by probabilistic vectors. The theory of quantization coefficients for infinite systems has significant differences from the finite case. One of these differences is the lack of finite maximal antichains, and another is the fact that the set of contraction ratios has zero infimum; another difference resides in the specific geometry of S and of its noncompact limit set J . We prove that, for each r ∈ ( 0 , ∞ ) , there exists a unique positive number κ r , so that for any κ \u3c κ r \u3c κ \u27 , the κ -dimensional lower quantization coefficient of order r for μ is positive, and we ...
Let m be a centered Gaussian measure on a separable Banach space E and N a positive integer. We stud...
In this paper using the Banach limit we have determined a Gibbs-like measure μ h supported by a cook...
Quantization is intrinsic to several data acquisition systems. This process is especially important ...
We investigate quantization coefficients for probability measures μ on limit sets, which are generat...
We consider condensation measures of the form P:=13P∘S−11+13P∘S−12+13ν associated with the system (S...
We introduce the quantization number and the essential covering rate. We treat the quantization for ...
The quantization scheme in probability theory deals with finding a best approximation of a given pro...
The problem of quantization can be thought of as follows: given a probability distribution P and a n...
We introduce the quantization number and the essential covering rate. We treat the quantization for ...
Quantization for a probability distribution refers to the idea of estimating a given probability by ...
The term quantization refers to the process of estimating a given probability by a discrete probabil...
We provide a full picture of the upper quantization dimension in term of the R\'enyi dimension, in t...
Quantization for probability distributions concerns the best approximation of a d-dimensional probab...
We obtain an almost everywhere quantifier elimination for (the non-critical fragment of) the logic w...
AbstractThe quantization dimension function for a probability measure induced by a set of infinite c...
Let m be a centered Gaussian measure on a separable Banach space E and N a positive integer. We stud...
In this paper using the Banach limit we have determined a Gibbs-like measure μ h supported by a cook...
Quantization is intrinsic to several data acquisition systems. This process is especially important ...
We investigate quantization coefficients for probability measures μ on limit sets, which are generat...
We consider condensation measures of the form P:=13P∘S−11+13P∘S−12+13ν associated with the system (S...
We introduce the quantization number and the essential covering rate. We treat the quantization for ...
The quantization scheme in probability theory deals with finding a best approximation of a given pro...
The problem of quantization can be thought of as follows: given a probability distribution P and a n...
We introduce the quantization number and the essential covering rate. We treat the quantization for ...
Quantization for a probability distribution refers to the idea of estimating a given probability by ...
The term quantization refers to the process of estimating a given probability by a discrete probabil...
We provide a full picture of the upper quantization dimension in term of the R\'enyi dimension, in t...
Quantization for probability distributions concerns the best approximation of a d-dimensional probab...
We obtain an almost everywhere quantifier elimination for (the non-critical fragment of) the logic w...
AbstractThe quantization dimension function for a probability measure induced by a set of infinite c...
Let m be a centered Gaussian measure on a separable Banach space E and N a positive integer. We stud...
In this paper using the Banach limit we have determined a Gibbs-like measure μ h supported by a cook...
Quantization is intrinsic to several data acquisition systems. This process is especially important ...