scholarly journals Weak and Strong Limit Theorems for Stochastic Processes under Nonadditive Probability

2014 ◽  
Vol 2014 ◽  
pp. 1-7
Author(s):  
Xiaoyan Chen ◽  
Zengjing Chen
Keyword(s):  
Continuous Function ◽  
Stochastic Processes ◽  
Function Space ◽  
Limit Theorems ◽  
Linear Interpolation ◽  
Strong Limit ◽  
Probability Limit ◽  
Large Numbers ◽  
Upper Probability ◽  
Continuous Function Space

This paper extends laws of large numbers under upper probability to sequences of stochastic processes generated by linear interpolation. This extension characterizes the relation between sequences of stochastic processes and subsets of continuous function space in the framework of upper probability. Limit results for sequences of functional random variables and some useful inequalities are also obtained as applications.

The Erdős–Rényi Law and Strong Limit Theorems of Probability

2021 ◽  
Vol 58 (2) ◽  
pp. 263-273
Author(s):  
Andrei N. Frolov
Keyword(s):  
Limit Theorems ◽  
Law Of Large Numbers ◽  
Strong Limit ◽  
Large Numbers ◽  
Famous Paper

Fifty years ago P. Erdős and A. Rényi published their famous paper on the new law of large numbers. In this survey, we describe numerous results and achievements which are related with this paper or motivated by it during these years.

Functional limit theorems for stochastic processes based on embedded processes

1975 ◽  
Vol 7 (01) ◽  
pp. 123-139 ◽  
Author(s):  
Richard F. Serfozo
Keyword(s):  
Stochastic Process ◽  
Stochastic Processes ◽  
Limit Theorems ◽  
Functional Limit ◽  
Functional Limit Theorems ◽  
Limit Laws ◽  
Iterated Logarithm ◽  
Large Numbers ◽  
Subordinated Processes ◽  
Analogous Functional

The techniques used by Doeblin and Chung to obtain ordinary limit laws (central limit laws, weak and strong laws of large numbers, and laws of the iterated logarithm) for Markov chains, are extended to obtain analogous functional limit laws for stochastic processes which have embedded processes satisfying these laws. More generally, it is shown how functional limit laws of a stochastic process are related to those of a process embedded in it. The results herein unify and extend many existing limit laws for Markov, semi-Markov, queueing, regenerative, semi-stationary, and subordinated processes.

Probability and Its Application in Britain during the 17th and 18th Centuries

2017 ◽  
Author(s):  
David Bellhouse
Keyword(s):  
World War Ii ◽  
Stochastic Processes ◽  
20Th Century ◽  
Limit Theorems ◽  
Natural Sciences ◽  
Independent Random Variables ◽  
World War ◽  
Strong Limit ◽  
Gradual Introduction ◽  
Mathematical Quality

In the 18th and 19th centuries, probability was a part of moral and natural sciences, rather than of mathematics. Still, since Laplace’s 1812 Théorie analytique des probabilités, specific analytic methods of probability aroused the interest of mathematicians, and probability began to develop a purely mathematical quality. In the 20th century the mathematical essence reached full autonomy and constituted “modern” probability. Significant in this development was the gradual introduction of a measure theoretic framework. In this way, the main subfields of modern probability, as axiomatics, weak and strong limit theorems, sequences of non-independent random variables, and stochastic processes, could be integrated into a well-connected complex until World War II.

scholarly journals Strong Limit Theorems for Dependent Random Variables

2021 ◽  
Vol 15 ◽  
pp. 15-17
Author(s):  
Libin Wu ◽  
Bainian Li
Keyword(s):  
Limit Theorems ◽  
Complete Convergence ◽  
Law Of Large Numbers ◽  
Random Variables ◽  
Moment Inequality ◽  
Strong Limit ◽  
Dependent Random Variables ◽  
Strong Law ◽  
Large Numbers ◽  
Independent And Identically Distributed

In this article We establish moment inequality of dependent random variables, furthermore some theorems of strong law of large numbers and complete convergence for sequences of dependent random variables. In particular, independent and identically distributed Marcinkiewicz Law of large numbers are generalized to the case of m₀ -dependent sequences.

Functional limit theorems for stochastic processes based on embedded processes

1975 ◽  
Vol 7 (1) ◽  
pp. 123-139 ◽  
Author(s):  
Richard F. Serfozo
Keyword(s):  
Stochastic Process ◽  
Stochastic Processes ◽  
Limit Theorems ◽  
Functional Limit ◽  
Functional Limit Theorems ◽  
Limit Laws ◽  
Iterated Logarithm ◽  
Large Numbers ◽  
Subordinated Processes ◽  
Analogous Functional

The techniques used by Doeblin and Chung to obtain ordinary limit laws (central limit laws, weak and strong laws of large numbers, and laws of the iterated logarithm) for Markov chains, are extended to obtain analogous functional limit laws for stochastic processes which have embedded processes satisfying these laws. More generally, it is shown how functional limit laws of a stochastic process are related to those of a process embedded in it. The results herein unify and extend many existing limit laws for Markov, semi-Markov, queueing, regenerative, semi-stationary, and subordinated processes.

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