scholarly journals Measures of finite (r,p)-energy and potentials on a separable metric space

1992 ◽  
pp. 415-444 ◽  
Author(s):  
Tetsuya Kazumi ◽  
Ichiro Shigekawa
Keyword(s):  
Metric Space ◽  
Separable Metric Space

scholarly journals On limit theorems for random fields

2009 ◽  
Vol 50 ◽  
Author(s):  
Rimas Banys
Keyword(s):  
Metric Space ◽  
Random Fields ◽  
Limit Theorems ◽  
Characteristic Property ◽  
Complete Separable Metric Space ◽  
Separable Metric Space

A complete separable metric space of functions defined on the positive quadrant of the plane is constructed. The characteristic property of these functions is that at every point x there exist two lines intersecting at this point such that limits limy→x f (y) exist when y approaches x along any path not intersecting these lines. A criterion of compactness of subsets of this space is obtained.

scholarly journals DIMENSIONS IN A SEPARABLE METRIC SPACE

1995 ◽  
Vol 49 (1) ◽  
pp. 143-162 ◽  
Author(s):  
Masakazu TAMASHIRO
Keyword(s):  
Metric Space ◽  
Separable Metric Space

scholarly journals Super-extremal processes and the argmax process

1994 ◽  
Vol 31 (04) ◽  
pp. 958-978 ◽  
Author(s):  
Sidney I. Resnick ◽  
Rishin Roy
Keyword(s):  
Metric Space ◽  
Semicontinuous Function ◽  
Closed Set ◽  
Continuous Choice ◽  
Extremal Processes ◽  
Upper Semicontinuous Function ◽  
Classical Vector ◽  
Vector Valued ◽  
Path Properties ◽  
Separable Metric Space

In this paper, we develop the probabilistic foundations of the dynamic continuous choice problem. The underlying choice set is a compact metric space E such as the unit interval or the unit square. At each time point t, utilities for alternatives are given by a random function . To achieve a model of dynamic continuous choice, the theory of classical vector-valued extremal processes is extended to super-extremal processes Y = {Yt, t > 0}. At any t > 0, Y t is a random upper semicontinuous function on a locally compact, separable, metric space E. General path properties of Y are discussed and it is shown that Y is Markov with state-space US(E). For each t > 0, Y t is associated. For a compact metric E, we consider the argmax process M = {Mt, t > 0}, where . In the dynamic continuous choice application, the argmax process M represents the evolution of the set of random utility maximizing alternatives. M is a closed set-valued random process, and its path properties are investigated.

Uniform Embeddings into Hilbert Space and a Question of Gromov

2002 ◽  
Vol 45 (1) ◽  
pp. 60-70 ◽  
Author(s):  
A. N. Dranishnikov ◽  
G. Gong ◽  
V. Lafforgue ◽  
G. Yu
Keyword(s):  
Hilbert Space ◽  
Metric Space ◽  
Uniform Embedding ◽  
Separable Metric Space

AbstractGromov introduced the concept of uniform embedding into Hilbert space and asked if every separable metric space admits a uniform embedding into Hilbert space. In this paper, we study uniform embedding into Hilbert space and answer Gromov’s question negatively.

scholarly journals Note on limits of simply continuous and cliquish functions

1994 ◽  
Vol 17 (3) ◽  
pp. 447-450 ◽  
Author(s):  
Janina Ewert
Keyword(s):  
Metric Space ◽  
Continuous Functions ◽  
Baire Space ◽  
Baire Property ◽  
Baire Class ◽  
Class 1 ◽  
Pointwise Limit ◽  
Separable Metric Space

The main result of this paper is that any functionfdefined on a perfect Baire space(X,T)with values in a separable metric spaceYis cliquish (has the Baire property) iff it is a uniform (pointwise) limit of sequence{fn:n≥1}of simply continuous functions. This result is obtained by a change of a topology onXand showing that a functionf:(X,T)→Yis cliquish (has the Baire property) iff it is of the Baire class 1 (class 2) with respect to the new topology.

scholarly journals A Universal Separable Diversity

2017 ◽  
Vol 5 (1) ◽  
pp. 138-151 ◽  
Author(s):  
David Bryant ◽  
André Nies ◽  
Paul Tupper
Keyword(s):  
Metric Space ◽  
Metric Spaces ◽  
Complete Metric Space ◽  
Urysohn Space ◽  
Fundamental Properties ◽  
Finite Set ◽  
Whole Space ◽  
Set Of Points ◽  
Separable Metric Space

AbstractThe Urysohn space is a separable complete metric space with two fundamental properties: (a) universality: every separable metric space can be isometrically embedded in it; (b) ultrahomogeneity: every finite isometry between two finite subspaces can be extended to an auto-isometry of the whole space. The Urysohn space is uniquely determined up to isometry within separable metric spaces by these two properties. We introduce an analogue of the Urysohn space for diversities, a recently developed variant of the concept of a metric space. In a diversity any finite set of points is assigned a non-negative value, extending the notion of a metric which only applies to unordered pairs of points.We construct the unique separable complete diversity that it is ultrahomogeneous and universal with respect to separable diversities.

Asymptotic stationarity of multichannel queues

1993 ◽  
Vol 25 (1) ◽  
pp. 203-220 ◽  
Author(s):  
Władysław Szczotka
Keyword(s):  
Metric Space ◽  
Separable Metric Space

The paper deals with asymptotic stationarity of the process where is a vector in with non-negative coordinates, is an -valued process, S is a separable metric space and all operations in are meant in the coordinate-wise sense. It is shown that a type of asymptotic stationarity of (X, Y), together with some conditions, implies the same type of asymptotic stationarity of (w, X, Y). This result is applied to analyze asymptotic stationarity of multichannel queues. It may also be used to analyze asymptotic stationarity of series of multichannel queues.

WEAKLY MIXING IMPLIES DISTRIBUTIONAL CHAOS IN A SEQUENCE

2010 ◽  
Vol 24 (14) ◽  
pp. 1595-1600 ◽  
Author(s):  
LIDONG WANG ◽  
YU'E YANG ◽  
ZHENYAN CHU ◽  
GONGFU LIAO
Keyword(s):  
Dynamical Systems ◽  
Metric Space ◽  
Distributional Chaos ◽  
Weakly Mixing ◽  
Separable Metric Space

Let X be a separable metric space containing at least two points, and f:X→X be continuous. In this paper, we consider dynamical systems on X, and prove that topologically weakly mixing implies distributional chaos in a sequence.

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