Spelling suggestions: "subject:"markov process."" "subject:"darkov process.""
1 
Mechanisms of cracking for iron base alloys in hot aqueous solutionsZhou, X. Y. January 1996 (has links)
No description available.

2 
Ruin theory under Markovian regimeswitching risk modelsZhu, Jinxia. January 2008 (has links)
Thesis (Ph. D.)University of Hong Kong, 2008. / Includes bibliographical references (leaf 159165) Also available in print.

3 
A Mathematical Model of Amoeboid Cell Motion as a ContinuousTime Markov ProcessDespain, Lynnae 01 March 2015 (has links) (PDF)
Understanding cell motion facilitates the understanding of many biological processes such as wound healing and cancer growth. Constructing mathematical models that replicate amoeboid cell motion can help us understand and make predictions about realworld cell movement. We review a forcebased model of cell motion that considers a cell as a nucleus and several adhesion sites connected to the nucleus by springs. In this model, the cell moves as the adhesion sites attach to and detach from a substrate. This model is then reformulated as a random process that tracks the attachment characteristic (attached or detached) of each adhesion site, the location of each adhesion site, and the centroid of the attached sites. It is shown that this random process is a continuoustime jumptype Markov process and that the subprocess that counts the number of attached adhesion sites is also a Markov process with an attracting invariant distribution. Under certain hypotheses, we derive a formula for the velocity of the expected location of the centroid.

4 
Ruin theory under Markovian regimeswitching risk modelsZhu, Jinxia., 朱金霞. January 2008 (has links)
published_or_final_version / Statistics and Actuarial Science / Doctoral / Doctor of Philosophy

5 
Stochastic Mortality ModellingLiu, Xiaoming 28 July 2008 (has links)
For life insurance and annuity products whose payoffs depend on the future mortality rates, there is a risk that realized
mortality rates will be different from the anticipated rates
accounted for in their pricing and reserving calculations. This is
termed as mortality risk. Since mortality risk is difficult to
diversify and has significant financial impacts on insurance
policies and pension plans, it is now a wellaccepted fact that
stochastic approaches shall be adopted to model the mortality risk
and to evaluate the mortalitylinked securities.
The objective of this thesis is to propose the use of a
timechanged Markov process to describe stochastic mortality
dynamics for pricing and risk management purposes. Analytical and
empirical properties of this dynamics have been investigated using
a matrixanalytic methodology. Applications of the proposed model
in the evaluation of fair values for mortality linked securities
have also been explored.
To be more specific, we consider a finitestate Markov process
with one absorbing state. This Markov process is related to an
underlying aging mechanism and the survival time is viewed as the
time until absorption. The resulting distribution for the survival
time is a socalled phasetype distribution. This approach is
different from the traditional curve fitting mortality models in
the sense that the survival probabilities are now linked with an
underlying Markov aging process. Markov mathematical and
phasetype distribution theories therefore provide us a flexible
and tractable framework to model the mortality dynamics. And the
timechanged Markov process allows us to incorporate the
uncertainties embedded in the future mortality evolution.
The proposed model has been applied to price the EIB/BNP Longevity
Bonds and other mortality derivatives under the independent
assumption of interest rate and mortality rate. A calibrating
method for the model is suggested so that it can utilize both the
market price information involving the relevant mortality risk and
the latest mortality projection. The proposed model has also been
fitted to various type of population mortality data for empirical
study. The fitting results show that our model can interpret the
stylized mortality patterns very well.

6 
Epidemics on complex networksSanatkar, Mohammad Reza January 1900 (has links)
Master of Science / Department of Electrical and Computer Engineering / Karen Garrett / Bala Natarajan / Caterina Scoglio / In this thesis, we propose a statistical model to predict disease dispersal in dynamic networks. We model the process of disease spreading using discrete time Markov chain. In this case, the vector of probability of infection is the state vector and every element of the state vector is a continuous variable between zero and one. In discrete time Markov chains, state probability vectors in each time step depends on state probability vector in the previous time step and one step transition probability matrix. The transition probability matrix can be time variant or time invariant. If this matrix’s elements are functions of elements of vector state probability in previous step, the corresponding Markov chain is non linear dynamical system. However, if those elements are independent of vector state probability, the corresponding Markov chain is a linear dynamical system.
We especially focus on the dispersal of soybean rust. In our problem, we have a network of US counties and we aim at predicting that which counties are more likely to get infected by soybean rust during a year based on observations of soybean rust up to that time as well as corresponding observations to previous years. Other data such as soybean and kudzu densities in each county, daily wind data, and distance between counties helps us to build the model.
The rapid growth in the number of Internet users in recent years has led malware generators to exploit this potential to attack computer users around the word. Internet users are frequent targets of malicious software every day. The ability of malware to exploit the infrastructures of networks for propagation determines how detrimental they can be to the network’s security. Malicious software can make large outbreaks if they are able to exploit the structure of the Internet and interactions between users to propagate.
Epidemics typically start with some initial infected nodes. Infected nodes can cause their
healthy neighbors to become infected with some probability. With time and in some cases with external intervention, infected nodes can be cured and go back to a healthy state. The study of epidemic dispersals on networks aims at explaining how epidemics evolve and spread in networks. One of the most interesting questions regarding an epidemic spread in a network is whether the epidemic dies out or results in a massive outbreak. Epidemic threshold is a parameter that addresses this question by considering both the network topology and epidemic strength.

7 
Markov Process Modeling of A System Under WIPLOAD ControlQi, Chao, Appa Iyer, Sivakumar, Ganesan, Viswanath Kumar 01 1900 (has links)
This paper analyzes a proposed release controlmethodology, WIPLOAD Control (WIPLCtrl), using a transfer line case modeled by Markov process modeling methodology. The performance of WIPLCtrl is compared with that of CONWIP under 13 system configurations in terms of throughput, average inventory level, as well as average cycle time. As a supplement to the analytical model, a simulation model of the transfer line is used to observe the performance of the release control methodologies on the standard deviation of cycle time. From the analysis, we identify the system configurations in which the advantages of WIPLCtrl could be observed. / SingaporeMIT Alliance (SMA)

8 
Stochastic Mortality ModellingLiu, Xiaoming 28 July 2008 (has links)
For life insurance and annuity products whose payoffs depend on the future mortality rates, there is a risk that realized
mortality rates will be different from the anticipated rates
accounted for in their pricing and reserving calculations. This is
termed as mortality risk. Since mortality risk is difficult to
diversify and has significant financial impacts on insurance
policies and pension plans, it is now a wellaccepted fact that
stochastic approaches shall be adopted to model the mortality risk
and to evaluate the mortalitylinked securities.
The objective of this thesis is to propose the use of a
timechanged Markov process to describe stochastic mortality
dynamics for pricing and risk management purposes. Analytical and
empirical properties of this dynamics have been investigated using
a matrixanalytic methodology. Applications of the proposed model
in the evaluation of fair values for mortality linked securities
have also been explored.
To be more specific, we consider a finitestate Markov process
with one absorbing state. This Markov process is related to an
underlying aging mechanism and the survival time is viewed as the
time until absorption. The resulting distribution for the survival
time is a socalled phasetype distribution. This approach is
different from the traditional curve fitting mortality models in
the sense that the survival probabilities are now linked with an
underlying Markov aging process. Markov mathematical and
phasetype distribution theories therefore provide us a flexible
and tractable framework to model the mortality dynamics. And the
timechanged Markov process allows us to incorporate the
uncertainties embedded in the future mortality evolution.
The proposed model has been applied to price the EIB/BNP Longevity
Bonds and other mortality derivatives under the independent
assumption of interest rate and mortality rate. A calibrating
method for the model is suggested so that it can utilize both the
market price information involving the relevant mortality risk and
the latest mortality projection. The proposed model has also been
fitted to various type of population mortality data for empirical
study. The fitting results show that our model can interpret the
stylized mortality patterns very well.

9 
Security Analysis and Improvement Model for Webbased ApplicationsWang, Yong 14 January 2010 (has links)
Today the web has become a major conduit for information. As the World Wide
Web?s popularity continues to increase, information security on the web has become an
increasing concern. Web information security is related to availability, confidentiality,
and data integrity. According to the reports from http://www.securityfocus.com in May
2006, operating systems account for 9% vulnerability, webbased software systems
account for 61% vulnerability, and other applications account for 30% vulnerability.
In this dissertation, I present a security analysis model using the Markov Process
Model. Risk analysis is conducted using fuzzy logic method and information entropy
theory. In a webbased application system, security risk is most related to the current
states in software systems and hardware systems, and independent of web application
system states in the past. Therefore, the webbased applications can be approximately
modeled by the Markov Process Model. The webbased applications can be conceptually
expressed in the discrete states of (web_client_good; web_server_good,
web_server_vulnerable, web_server_attacked, web_server_security_failed; database_server_good, database_server_vulnerable, database_server_attacked,
database_server_security_failed) as state space in the Markov Chain. The vulnerable
behavior and system response in the webbased applications are analyzed in this
dissertation. The analyses focus on functional availabilityrelated aspects: the probability
of reaching a particular security failed state and the mean time to the security failure of a
system. Vulnerability risk index is classified in three levels as an indicator of the level of
security (low level, high level, and failed level). An illustrative application example is
provided. As the second objective of this dissertation, I propose a security improvement
model for the webbased applications using the GeoIP services in the formal methods. In
the security improvement model, web access is authenticated in rolebased access control
using user logins, remote IP addresses, and physical locations as subject credentials to
combine with the requested objects and privilege modes. Access control algorithms are
developed for subjects, objects, and access privileges. A secure implementation
architecture is presented. In summary, the dissertation has developed security analysis
and improvement model for the webbased application. Future work will address Markov
Process Model validation when security data collection becomes easy. Security
improvement model will be evaluated in performance aspect.

10 
Aspects of stochastic control and switching: from Parrondo’s games to electrical circuits.Allison, Andrew Gordon January 2009 (has links)
The first half of this thesis deals with the line of thought that leads to the development of discrete games of chance as models in statistical physics, with an emphasis on analysis of Parrondo’s games. The second half of the thesis is concerned with applying discrete games of chance to the modelling of other phenomena in the discipline of electrical engineering. The important features being the element of switching that is implicit in discrete games of chance and the element of uncertainty, introduced by the random aspect of discrete games of chance. / http://proxy.library.adelaide.edu.au/login?url= http://library.adelaide.edu.au/cgibin/Pwebrecon.cgi?BBID=1474722 / Thesis (Ph.D.)  University of Adelaide, School of Electrical and Electronic Engineering, 2009

Page generated in 0.0634 seconds