scholarly journals Sensitivity Analysis of Some Applied Probability Models

2021 ◽  
Author(s):  
E. V. Bulinskaya ◽  
B. I. Shigida
Keyword(s):  
Sensitivity Analysis ◽  
Applied Probability ◽  
Probability Models

Semi-Markov Replacement Chains

1994 ◽  
Vol 26 (3) ◽  
pp. 728-755 ◽  
Author(s):  
Ioannis I. Gerontidis
Keyword(s):  
Markov Chain ◽  
Control Theory ◽  
Markov Chains ◽  
Time Dependent ◽  
Applied Probability ◽  
Probability Models ◽  
Natural Manner ◽  
Environmental Behaviour ◽  
New Process

We consider an absorbing semi-Markov chain for which each time absorption occurs there is a resetting of the chain according to some initial (replacement) distribution. The new process is a semi-Markov replacement chain and we study its properties in terms of those of the imbedded Markov replacement chain. A time-dependent version of the model is also defined and analysed asymptotically for two types of environmental behaviour, i.e. either convergent or cyclic. The results contribute to the control theory of semi-Markov chains and extend in a natural manner a wide variety of applied probability models. An application to the modelling of populations with semi-Markovian replacements is also presented.

Semi-Markov Replacement Chains

1994 ◽  
Vol 26 (03) ◽  
pp. 728-755 ◽  
Author(s):  
Ioannis I. Gerontidis
Keyword(s):  
Markov Chain ◽  
Control Theory ◽  
Markov Chains ◽  
Time Dependent ◽  
Applied Probability ◽  
Probability Models ◽  
Natural Manner ◽  
Environmental Behaviour ◽  
New Process

We consider an absorbing semi-Markov chain for which each time absorption occurs there is a resetting of the chain according to some initial (replacement) distribution. The new process is a semi-Markov replacement chain and we study its properties in terms of those of the imbedded Markov replacement chain. A time-dependent version of the model is also defined and analysed asymptotically for two types of environmental behaviour, i.e. either convergent or cyclic. The results contribute to the control theory of semi-Markov chains and extend in a natural manner a wide variety of applied probability models. An application to the modelling of populations with semi-Markovian replacements is also presented.

scholarly journals Dominance Degree Multiple Attribute Decision Making Based on Z-Number Cognitive Information

2019 ◽  
Vol 2019 ◽  
pp. 1-19 ◽  
Author(s):  
Aiting Yao ◽  
Junjun Mao ◽  
Minchao Wu ◽  
Tao Wu
Keyword(s):  
Decision Making ◽  
Sensitivity Analysis ◽  
Decision Model ◽  
Multiple Attribute Decision Making ◽  
Cross Entropy ◽  
Probability Models ◽  
Multiple Attribute ◽  
Cognitive Information ◽  
Relationship Of ◽  
The Impact

Cognitive information can be described by Z-number fully and effectively. However, many problems of Z-number need to be further studied. In this paper, two hidden probability models for calculating Z-number are established to provide more intuitive and abundant information. Next, the dominance degree relationship of Z-number is developed and subdivided. Furthermore, combined with the hidden probability of calculation, three different measurements of dominance degree are defined from three levels of geometry, algebra, and cross entropy based on the outranking relationship. The influencing factors are analyzed for different combinations of two probability models and three dominance degree measures. A multiattribute decision model is established on the basis of new grey association analysis and QUALIFLEX method. Finally, a decision example is given to verify the effectiveness and feasibility of the method. And sensitivity analysis is made to determine the impact of parameters and hidden probability on the decision model.

Computer experimentation in applied probability

1988 ◽  
Vol 25 (A) ◽  
pp. 31-43 ◽  
Author(s):  
Marcel F. Neuts
Keyword(s):  
Experimental Study ◽  
Applied Probability ◽  
Empirical Methods ◽  
Factual Information ◽  
Maximal Eigenvalue ◽  
Probability Models ◽  
Random Elements ◽  
Positive Matrices ◽  
Empirical Methodology ◽  
Great Complexity

This is a discussion of the place of empirical methods in the study of probability models. It is argued that, for models of great complexity, careful experimentation may be a legitimate source of factual information on the behavior of such models. The need for rigorous standards of empirical methodology and of appropriate reporting is stressed. A simple, but interesting question on the distribution of the maximal eigenvalue of positive matrices with random elements is used as an example of an experimental study. In the course of the discussion of that example, several operational rules of sound computer experimentation are stated.

Computer experimentation in applied probability

1988 ◽  
Vol 25 (A) ◽  
pp. 31-43
Author(s):  
Marcel F. Neuts
Keyword(s):  
Experimental Study ◽  
Applied Probability ◽  
Empirical Methods ◽  
Factual Information ◽  
Maximal Eigenvalue ◽  
Probability Models ◽  
Random Elements ◽  
Positive Matrices ◽  
Empirical Methodology ◽  
Great Complexity

This is a discussion of the place of empirical methods in the study of probability models. It is argued that, for models of great complexity, careful experimentation may be a legitimate source of factual information on the behavior of such models. The need for rigorous standards of empirical methodology and of appropriate reporting is stressed. A simple, but interesting question on the distribution of the maximal eigenvalue of positive matrices with random elements is used as an example of an experimental study. In the course of the discussion of that example, several operational rules of sound computer experimentation are stated.

Applied Probability Models

2002 ◽  
Vol 56 (3) ◽  
pp. 248-248
Author(s):  
Stergios B Fotopoulos
Keyword(s):  
Applied Probability ◽  
Probability Models
Sign in / Sign up

Export Citation Format

Share Document