Dissertations / Theses on the topic 'Markov chain model'
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Yildirak, Sahap Kasirga. "The Identificaton Of A Bivariate Markov Chain Market Model." Phd thesis, METU, 2004. http://etd.lib.metu.edu.tr/upload/1257898/index.pdf.
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This work is an extension of the classical Cox-Ross-Rubinstein
discrete time market model in which only one risky asset is
considered. We introduce another risky asset into the model.
Moreover, the random structure of the asset price sequence is
generated by bivariate finite state Markov chain. Then, the
interest rate varies over time as it is the function of generating
sequences. We discuss how the model can be adapted to the real
data. Finally, we illustrate sample implementations to give a
better idea about the use of the model.
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Jindasawat, Jutaporn. "Testing the order of a Markov chain model." Thesis, University of Newcastle Upon Tyne, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.446197.
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Mehl, Christopher. "Bayesian Hierarchical Modeling and Markov Chain Simulation for Chronic Wasting Disease." Diss., University of Colorado at Denver, 2004. http://hdl.handle.net/10919/71563.
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In this thesis, a dynamic spatial model for the spread of Chronic Wasting Disease in Colorado mule deer is derived from a system of differential equations that captures the qualitative spatial and temporal behaviour of the disease. These differential equations are incorporated into an empirical Bayesian hierarchical model through the unusual step of deterministic autoregressive updates. Spatial effects in the model are described directly in the differential equations rather than through the use of correlations in the data. The use of deterministic updates is a simplification that reduces the number of parameters that must be estimated, yet still provides a flexible model that gives reasonable predictions for the disease. The posterior distribution generated by the data model hierarchy possesses characteristics that are atypical for many Markov chain Monte Carlo simulation techniques. To address these difficulties, a new MCMC technique is developed that has qualities similar to recently introduced tempered Langevin type algorithms. The methodology is used to fit the CWD model, and posterior parameter estimates are then used to obtain predictions about Chronic Wasting Disease.
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Au, Chi Yan. "Numerical methods for solving Markov chain driven Black-Scholes model." HKBU Institutional Repository, 2010. http://repository.hkbu.edu.hk/etd_ra/1154.
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Yapo, Patrice Ogou 1967. "A Markov chain flow model with application to flood forecasting." Thesis, The University of Arizona, 1992. http://hdl.handle.net/10150/278135.
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This thesis presents a new approach to streamflow forecasting. The approach is based on specifying the probabilities that the next flow of a stream will occur within different ranges of values. Hence, this method is different from the time series models where point estimates are given as forecasts. With this approach flood forecasting is possible by focusing on a preselected range of streamflows. A double criteria objective function is developed to assess the model performance in flood prediction. Three case studies are examined based on data from the Salt River in Phoenix, Arizona and Bird Creek near Sperry, Oklahoma. The models presented are: a first order Markov chain (FOMC), a second order Markov chain (SOMC), and a first order Markov chain with rainfall as an exogenous input (FOMCX). Three forecasts methodologies are compared among each other and against time series models. It is shown that the SOMC is better than the FOMC while the FOMCX is better than the time series models.
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Neuhoff, Daniel. "Reversible Jump Markov Chain Monte Carlo." Doctoral thesis, Humboldt-Universität zu Berlin, Wirtschaftswissenschaftliche Fakultät, 2016. http://dx.doi.org/10.18452/17461.
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Die vier in der vorliegenden Dissertation enthaltenen Studien beschäftigen sich vorwiegend mit dem dynamischen Verhalten makroökonomischer Zeitreihen. Diese Dynamiken werden sowohl im Kontext eines einfachen DSGE Modells, als auch aus der Sichtweise reiner Zeitreihenmodelle untersucht.The four studies of this thesis are concerned predominantly with the dynamics of macroeconomic time series, both in the context of a simple DSGE model, as well as from a pure time series modeling perspective.
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Kharbouch, Alaa Amin. "A bacterial algorithm for surface mapping using a Markov modulated Markov chain model of bacterial chemotaxis." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/36186.
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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.Includes bibliographical references (p. 83-85).
Bacterial chemotaxis is the locomotory response of bacteria to chemical stimuli. E. coli movement can be described as a biased random walk, and it is known that the general biological or evolutionary function is to increase exposure to some substances and reduce exposure to others. In this thesis we introduce an algorithm for surface mapping, which tracks the motion of a bacteria-like software agent (based on a low-level model of the biochemical network responsible for chemotaxis) on a surface or objective function. Towards that end, a discrete Markov modulated Markov chains model of the chemotaxis pathway is described and used. Results from simulations using one- and two-dimensional test surfaces show that the software agents, referred to as bacterial agents, and the surface mapping algorithm can produce an estimate which shares some broad characteristics with the surface and uncovers some features of it. We also demonstrate that the bacterial agent, when given the ability to reduce the value of the surface at locations it visits (analogous to consuming a substance on a concentration surface), is more effective in reducing the surface integral within a certain period of time when compared to a bacterial agent lacking the ability to sense surface information or respond to it.
by Alaa Amin Kharbouch.
S.M.
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Webb, Jared Anthony. "A Topics Analysis Model for Health Insurance Claims." BYU ScholarsArchive, 2013. https://scholarsarchive.byu.edu/etd/3805.
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Mathematical probability has a rich theory and powerful applications. Of particular note is the Markov chain Monte Carlo (MCMC) method for sampling from high dimensional distributions that may not admit a naive analysis. We develop the theory of the MCMC method from first principles and prove its relevance. We also define a Bayesian hierarchical model for generating data. By understanding how data are generated we may infer hidden structure about these models. We use a specific MCMC method called a Gibbs' sampler to discover topic distributions in a hierarchical Bayesian model called Topics Over Time. We propose an innovative use of this model to discover disease and treatment topics in a corpus of health insurance claims data. By representing individuals as mixtures of topics, we are able to consider their future costs on an individual level rather than as part of a large collective.
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Nasrallah, Yamen. "Enhanced IEEE 802.11.p-Based MAC Protocols for Vehicular Ad hoc Networks." Thesis, Université d'Ottawa / University of Ottawa, 2017. http://hdl.handle.net/10393/36168.
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The Intelligent Transportation System (ITS) is a cooperative system that relies on reliable and robust communication schemes among vehicles and between vehicles and their surroundings. The main objective of the ITS is to ensure the safety of vehicle drivers and pedestrians. It provides an efficient and reliable transportation system that enhances traffic management, reduces congestion time, enables smooth traffic re-routing, and avoids economic losses.
An essential part of the ITS is the Vehicular Ad hoc Network (VANET). VANET enables the setup of Vehicle-to-Vehicle (V2V) as well as Vehicle-to-Infrastructure (V2I) communication platforms: the two key components in the ITS. The de-facto standard used in wireless V2V and V2I communication applications is the Dedicated Short Range Communication (DSRC). The protocol that defines the specifications for the Medium Access Control (MAC) layer and the physical layer in the DSRC is the IEEE 802.11p protocol. The IEEE 802.11p protocol and its Enhanced Distributed Channel Access (EDCA) mechanism are the main focus of this thesis. Our main objective is to develop new IEEE 802.11p-based protocol for V2V and V2I communication systems, to improve the performance of safety-related applications. These applications are of paramount importance in ITS, because their goal is to decrease the rate of vehicle collisions, and hence reduce the enormous costs associated with them. In fact, large percentage of vehicle collisions can be easily avoided with the exchange of relevant information between vehicles and the Road Side Units (RSUs) installed on the sides of the roads.
In this thesis, we propose various enhancements to the IEEE 802.11p protocol to improve its performance by lowering the average end-to-end delay and increasing the average network throughput. We introduce multiple adaptive algorithms to promote the QoS support across all the Access Categories (AC) in IEEE 802.11p. We propose two adaptive backoff algorithms and two algorithms that adaptively change the values of the Arbitrary Inter-Frame Space (AIFS). Then we extend our model to be applied in a large-scale vehicular network. In this context, a multi-layer cluster-based architecture is adopted, and two new distributed time synchronization mechanisms are developed.
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Mamudu, Lohuwa. "Modeling Student Enrollment at ETSU Using a Discrete-Time Markov Chain Model." Digital Commons @ East Tennessee State University, 2017. https://dc.etsu.edu/etd/3310.
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Discrete-time Markov chain models can be used to make future predictions in many important fields including education. Government and educational institutions today are concerned about college enrollment and what impacts the number of students enrolling. One challenge is how to make an accurate prediction about student enrollment so institutions can plan appropriately. In this thesis, we model student enrollment at East Tennessee State University (ETSU) with a discrete-time Markov chain model developed using ETSU student data from Fall 2008 to Spring 2017. In this thesis, we focus on the progression from one level to another within the university system including graduation and dropout probabilities as indicated by the data. We further include the probability that a student will leave school for a limited period of time and then return to the institution. We conclude with a simulation of the model and a comparison to the trends seen in the data.
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Lindahl, John, and Douglas Persson. "Data-driven test case design of automatic test cases using Markov chains and a Markov chain Monte Carlo method." Thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-43498.
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Large and complex software that is frequently changed leads to testing challenges. It is well established that the later a fault is detected in software development, the more it costs to fix. This thesis aims to research and develop a method of generating relevant and non-redundant test cases for a regression test suite, to catch bugs as early in the development process as possible. The research was executed at Axis Communications AB with their products and systems in mind. The approach utilizes user data to dynamically generate a Markov chain model and with a Markov chain Monte Carlo method, strengthen that model. The model generates test case proposals, detects test gaps, and identifies redundant test cases based on the user data and data from a test suite. The sampling in the Markov chain Monte Carlo method can be modified to bias the model for test coverage or relevancy. The model is generated generically and can therefore be implemented in other API-driven systems. The model was designed with scalability in mind and further implementations can be made to increase the complexity and further specialize the model for individual needs.
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Pitt, Michael K. "Bayesian inference for non-Gaussian state space model using simulation." Thesis, University of Oxford, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.389211.
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Pagliarani, Andrea. "New markov chain based methods for single and cross-domain sentiment classification." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2015. http://amslaurea.unibo.it/8445/.
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Nowadays communication is switching from a centralized scenario, where communication media like newspapers, radio, TV programs produce information and people are just consumers, to a completely different decentralized scenario, where everyone is potentially an information producer through the use of social networks, blogs, forums that allow a real-time worldwide information exchange. These new instruments, as a result of their widespread diffusion, have started playing an important socio-economic role. They are the most used communication media and, as a consequence, they constitute the main source of information enterprises, political parties and other organizations can rely on. Analyzing data stored in servers all over the world is feasible by means of Text Mining techniques like Sentiment Analysis, which aims to extract opinions from huge amount of unstructured texts. This could lead to determine, for instance, the user satisfaction degree about products, services, politicians and so on. In this context, this dissertation presents new Document Sentiment Classification methods based on the mathematical theory of Markov Chains. All these approaches bank on a Markov Chain based model, which is language independent and whose killing features are simplicity and generality, which make it interesting with respect to previous sophisticated techniques. Every discussed technique has been tested in both Single-Domain and Cross-Domain Sentiment Classification areas, comparing performance with those of other two previous works. The performed analysis shows that some of the examined algorithms produce results comparable with the best methods in literature, with reference to both single-domain and cross-domain tasks, in $2$-classes (i.e. positive and negative) Document Sentiment Classification. However, there is still room for improvement, because this work also shows the way to walk in order to enhance performance, that is, a good novel feature selection process would be enough to outperform the state of the art. Furthermore, since some of the proposed approaches show promising results in $2$-classes Single-Domain Sentiment Classification, another future work will regard validating these results also in tasks with more than $2$ classes.
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Ayana, Haimanot, and Sarah Al-Swej. "A review of two financial market models: the Black--Scholes--Merton and the Continuous-time Markov chain models." Thesis, Mälardalens högskola, Akademin för utbildning, kultur och kommunikation, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-55417.
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The objective of this thesis is to review the two popular mathematical models of the financialderivatives market. The models are the classical Black–Scholes–Merton and the Continuoustime Markov chain (CTMC) model. We study the CTMC model which is illustrated by themathematician Ragnar Norberg. The thesis demonstrates how the fundamental results ofFinancial Engineering work in both models.The construction of the main financial market components and the approach used for pricingthe contingent claims were considered in order to review the two models. In addition, the stepsused in solving the first–order partial differential equations in both models are explained.The main similarity between the models are that the financial market components are thesame. Their contingent claim is similar and the driving processes for both models utilizeMarkov property.One of the differences observed is that the driving process in the BSM model is the Brownianmotion and Markov chain in the CTMC model.We believe that the thesis can motivate other students and researchers to do a deeper andadvanced comparative study between the two models.
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Walker, Neil Rawlinson. "A Bayesian approach to the job search model and its application to unemployment durations using MCMC methods." Thesis, University of Newcastle Upon Tyne, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.299053.
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Vaičiulytė, Ingrida. "Study and application of Markov chain Monte Carlo method." Doctoral thesis, Lithuanian Academic Libraries Network (LABT), 2014. http://vddb.library.lt/obj/LT-eLABa-0001:E.02~2014~D_20141209_112440-55390.
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Markov chain Monte Carlo adaptive methods by creating computationally effective algorithms for decision-making of data analysis with the given accuracy are analyzed in this dissertation. The tasks for estimation of parameters of the multivariate distributions which are constructed in hierarchical way (skew t distribution, Poisson-Gaussian model, stable symmetric vector law) are described and solved in this research. To create the adaptive MCMC procedure, the sequential generating method is applied for Monte Carlo samples, introducing rules for statistical termination and for sample size regulation of Markov chains. Statistical tasks, solved by this method, reveal characteristics of relevant computational problems including MCMC method.
Effectiveness of the MCMC algorithms is analyzed by statistical modeling method, constructed in the dissertation. Tests made with sportsmen data and financial data of enterprises, belonging to health-care industry, confirmed that numerical properties of the method correspond to the theoretical model. The methods and algorithms created also are applied to construct the model for sociological data analysis. Tests of algorithms have shown that adaptive MCMC algorithm allows to obtain estimators of examined distribution parameters in lower number of chains, and reducing the volume of calculations approximately two times. The algorithms created in this dissertation can be used to test the systems of stochastic type and to solve other statistical... [to full text]Disertacijoje nagrinėjami Markovo grandinės Monte-Karlo (MCMC) adaptavimo metodai, skirti efektyviems skaitiniams duomenų analizės sprendimų priėmimo su iš anksto nustatytu patikimumu algoritmams sudaryti. Suformuluoti ir išspręsti hierarchiniu būdu sudarytų daugiamačių skirstinių (asimetrinio t skirstinio, Puasono-Gauso modelio, stabiliojo simetrinio vektoriaus dėsnio) parametrų vertinimo uždaviniai. Adaptuotai MCMC procedūrai sukurti yra pritaikytas nuoseklaus Monte-Karlo imčių generavimo metodas, įvedant statistinį stabdymo kriterijų ir imties tūrio reguliavimą. Statistiniai uždaviniai išspręsti šiuo metodu leidžia atskleisti aktualias MCMC metodų skaitmeninimo problemų ypatybes. MCMC algoritmų efektyvumas tiriamas pasinaudojant disertacijoje sudarytu statistinio modeliavimo metodu. Atlikti eksperimentai su sportininkų duomenimis ir sveikatos industrijai priklausančių įmonių finansiniais duomenimis patvirtino, kad metodo skaitinės savybės atitinka teorinį modelį. Taip pat sukurti metodai ir algoritmai pritaikyti sociologinių duomenų analizės modeliui sudaryti. Atlikti tyrimai parodė, kad adaptuotas MCMC algoritmas leidžia gauti nagrinėjamų skirstinių parametrų įvertinius per mažesnį grandžių skaičių ir maždaug du kartus sumažinti skaičiavimų apimtį. Disertacijoje sukonstruoti algoritmai gali būti pritaikyti stochastinio pobūdžio sistemų tyrimui ir kitiems statistikos uždaviniams spręsti MCMC metodu.
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Magalhães, Cloé Leal de. "How bank lending affects firms' lifecycle : a Markov chain approach." Master's thesis, Instituto Superior de Economia e Gestão, 2019. http://hdl.handle.net/10400.5/19182.
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Mestrado em Econometria Aplicada e PrevisãoEsta dissertação analisa o impacto da concessão de crédito adicional a empresas não rentáveis sobre a sua probabilidade de se manterem não rentáveis, recuperarem para empresas rentáveis ou para saírem do mercado. Esta avaliação é efetuada através da estimação de um processo de Markov condicional à existência de crédito adicional, usando as estimativas do modelo logit multinomial. A aplicação deste modelo aos dados ao nível da empresa e do banco para Portugal entre 2011 e 2015 mostra que a concessão de crédito adicional teve um impacto positivo nas taxas de sobrevivência e recuperação das empresas não rentáveis, em contradição com alguma investigação recente sobre o tema.
This dissertation analyses how additional loans granted to non-profitable firms affect their probability to remain non-profitable, recover to profitable or exit the market. This assessment is carried out through the estimation of a Markov process conditional to the existence of additional bank loans, using the multinomial logit model estimates. Applying this model to Portuguese firm and bank level data from 2011 to 2015, the results point to a positive effect of additional bank loans over survival and recovery rates of non-profitable firms, contradicting some recent research on this topic.
info:eu-repo/semantics/publishedVersion
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Lu, Pingbo. "Calibrated Bayes factors for model selection and model averaging." The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1343396705.
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Keller, Peter, Sylvie Roelly, and Angelo Valleriani. "A quasi-random-walk to model a biological transport process." Universität Potsdam, 2013. http://opus.kobv.de/ubp/volltexte/2013/6358/.
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Transport Molecules play a crucial role for cell viability. Amongst others, linear motors transport cargos along rope-like structures from one location of
the cell to another in a stochastic fashion. Thereby each step of the motor, either forwards or backwards, bridges a fixed distance. While moving along the rope the motor can also detach and is lost. We give here a mathematical formalization of such dynamics as a random process which is an extension of Random Walks, to which we add an absorbing state to model the detachment of the motor from the rope. We derive particular properties of such processes that have not been available before. Our results include description of the maximal distance reached from the starting point and the position from which detachment takes place. Finally, we apply our theoretical results to a concrete established model of the transport molecule Kinesin V.
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Stettler, John. "The Discrete Threshold Regression Model." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1440369876.
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Byng, Martyn Charles. "A statistical model for locating regulatory regions in novel DNA sequences." Thesis, University of Reading, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.369119.
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Polisetti, Haritha. "Hidden Markov Chain Analysis: Impact of Misclassification on Effect of Covariates in Disease Progression and Regression." Scholar Commons, 2016. http://scholarcommons.usf.edu/etd/6568.
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Most of the chronic diseases have a well-known natural staging system through which the disease progression is interpreted. It is well established that the transition rates from one stage of disease to other stage can be modeled by multi state Markov models. But, it is also well known that the screening systems used to diagnose disease states may subject to error some times. In this study, a simulation study is conducted to illustrate the importance of addressing for misclassification in multi-state Markov models by evaluating and comparing the estimates for the disease progression Markov model with misclassification opposed to disease progression Markov model. Results of simulation study support that models not accounting for possible misclassification leads to bias. In order to illustrate method of accounting for misclassification is illustrated using dementia data which was staged as no cognitive impairment, mild cognitive impairment and dementia and diagnosis of dementia stage is prone to error sometimes. Subjects entered the study irrespective of their state of disease and were followed for one year and their disease state at follow up visit was recorded. This data is used to illustrate that application of multi state Markov model which is an example of Hidden Markov model in accounting for misclassification which is based on an assumption that the observed (misclassified) states conditionally depend on the underlying true disease states which follow the Markov process. The misclassification probabilities for all the allowed disease transitions were also estimated. The impact of misclassification on the effect of covariates is estimated by comparing the hazard ratios estimated by fitting data with progression multi state model and by fitting data with multi state model with misclassification which revealed that if misclassification has not been addressed the results are biased. Results suggest that the gene apoe ε4 is significantly associated with disease progression from mild cognitive impairment to dementia but, this effect was masked when general multi state Markov model was used. While there is no significant relation is found for other transitions.
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Jeon, Juncheol. "Deterioration model for ports in the Republic of Korea using Markov chain Monte Carlo with multiple imputation." Thesis, University of Dundee, 2019. https://discovery.dundee.ac.uk/en/studentTheses/1cc538ea-1468-4d51-bcf8-711f8b9912f9.
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Condition of infrastructure is deteriorated over time as it gets older. It is the deterioration model that predicts how and when facilities will deteriorate over time. In most infrastructure management system, the deterioration model is a crucial element. Using the deterioration model, it is very helpful to estimate when repair will be carried out, how much will be needed for the maintenance of the entire facilities, and what maintenance costs will be required during the life cycle of the facility. However, the study of deterioration model for civil infrastructures of ports is still in its infancy. In particular, there is almost no related research in South Korea. Thus, this study aims to develop a deterioration model for civil infrastructure of ports in South Korea. There are various methods such as Deterministic, Stochastic, and Artificial Intelligence to develop deterioration model. In this research, Markov model using Markov chain theory, one of the Stochastic methods, is used to develop deterioration model for ports in South Korea. Markov chain is a probabilistic process among states. i.e., in Markov chain, transition among states follows some probability which is called as the transition probability. The key process of developing Markov model is to find this transition probability. This process is called calibration. In this study, the existing methods, Optimization method and Markov Chain Monte Carlo (MCMC), are reviewed, and methods to improve for these are presented. In addition, in this study, only a small amount of data are used, which causes distortion of the model. Thus, supplement techniques are presented to overcome the small size of data. In order to address the problem of the existing methods and the lack of data, the deterioration model developed by the four calibration methods: Optimization, Optimization with Bootstrap, MCMC (Markov Chain Monte Carlo), and MCMC with Multiple imputation, are finally proposed in this study. In addition, comparison between four models are carried out and good performance model is proposed. This research provides deterioration model for port in South Korea, and more accurate calibration technique is suggested. Furthermore, the method of supplementing insufficient data has been combined with existing calibration techniques.
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Miazhynskaia, Tatiana, Sylvia Frühwirth-Schnatter, and Georg Dorffner. "A comparison of Bayesian model selection based on MCMC with an application to GARCH-type models." SFB Adaptive Information Systems and Modelling in Economics and Management Science, WU Vienna University of Economics and Business, 2003. http://epub.wu.ac.at/586/1/document.pdf.
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This paper presents a comprehensive review and comparison of five computational methods for Bayesian model selection, based on MCMC simulations from posterior model parameter distributions. We apply these methods to a well-known and important class of models in financial time series analysis, namely GARCH and GARCH-t models for conditional return distributions (assuming normal and t-distributions). We compare their performance vis--vis the more common maximum likelihood-based model selection on both simulated and real market data. All five MCMC methods proved feasible in both cases, although differing in their computational demands. Results on simulated data show that for large degrees of freedom (where the t-distribution becomes more similar to a normal one), Bayesian model selection results in better decisions in favour of the true model than maximum likelihood. Results on market data show the feasibility of all model selection methods, mainly because the distributions appear to be decisively non-Gaussian.Series: Report Series SFB "Adaptive Information Systems and Modelling in Economics and Management Science"
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Servitja, Robert Maria. "A First Study on Hidden Markov Models and one Application in Speech Recognition." Thesis, Linköpings universitet, Matematisk statistik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-123912.
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Speech is intuitive, fast and easy to generate, but it is hard to index and easy to forget. What is more, listening to speech is slow. Text is easier to store, process and consume, both for computers and for humans, but writing text is slow and requires some intention. In this thesis, we study speech recognition which allows converting speech into text, making it easier both to create and to use information. Our tool of study is Hidden Markov Models which is one of the most important machine learning models in speech and language processing. The aim of this thesis is to do a rst study in Hidden Markov Models and understand their importance, particularly in speech recognition. We will go through three fundamental problems that come up naturally with Hidden Markov Models: to compute a likelihood of an observation sequence, to nd an optimal state sequence given an observation sequence and the model, and to adjust the model parameters. A solution to each problem will be given together with an example and the corresponding simulations using MatLab. The main importance lies in the last example, in which a rst approach to speech recognition will be done.
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Yang, GuoLu. "Modèle de transport complet en rivière avec granulométrie étendue." Grenoble 1, 1989. http://www.theses.fr/1989GRE10011.
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Les variations des lignes d'eau et du lit des rivieres alluvionnaires dans le cas du transport complet (charriage+suspension) des sediments en granulometrie etendue sont etudiees par un modele mathematique uni-dimensionnel. Dans ce modele le charriage et la suspension sont consideres comme deux phenomenes du transport en tenant compte d'un terme source-puits qui represente l'echange entre eux. Le terme source-puits est formule par un modele d'echanges stochastiques considerant trois etats: suspension, charriage et immobilite, les probabilites des etats sont obtenues par le processus de chaine de markov. Le modele conceptuel d'une "couche melangee" est introduit pour reproduire les phenomenes de pavage et de triage. Le systeme d'equations a resoudre est analyse par la methode des caracteristiques. Une solution numerique decouplee du systeme est presentee. Un nouvel algorithme, assurant le calcul couple du transport par convection-diffusion-reaction, est developpe. Des tests du modele mathematique sont systematiquement effectues afin d'examiner la sensibilite et montrer la precision du modele
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Orguner, Umut. "Improved State Estimation For Jump Markov Linear Systems." Phd thesis, METU, 2006. http://etd.lib.metu.edu.tr/upload/12607895/index.pdf.
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This thesis presents a comprehensive example
framework on how current multiple model state estimation algorithms for jump Markov linear systems can be improved. The possible improvements are categorized as:
-Design of multiple model state estimation algorithms using
new criteria.
-Improvements obtained using existing multiple model state
estimation algorithms.
In the first category, risk-sensitive estimation is proposed for
jump Markov linear systems. Two types of cost functions namely, the instantaneous and cumulative cost functions related with risk-sensitive estimation are examined and for each one, the
corresponding multiple model estate estimation algorithm is derived.
For the cumulative cost function, the derivation involves the
reference probability method where one defines and uses a new
probability measure under which the involved processes has
independence properties. The performance of the proposed
risk-sensitive filters are illustrated and compared with
conventional algorithms using simulations.
The thesis addresses the second category of improvements by
proposing
-Two new online transition probability estimation schemes for
jump Markov linear systems.
-A mixed multiple model state estimation scheme which combines
desirable properties of two different multiple model state
estimation methods.
The two online transition probability estimators proposed use the
recursive Kullback-Leibler (RKL) procedure and the maximum
likelihood (ML) criteria to derive the corresponding identification
schemes. When used in state estimation, these methods result in an average error decrease in the root mean square (RMS) state
estimation errors, which is proved using simulation studies.
The mixed multiple model estimation procedure
which utilizes the analysis of the single Gaussian approximation of Gaussian mixtures in Bayesian filtering, combines IMM (Interacting Multiple Model) filter and GPB2 (2nd Order Generalized Pseudo Bayesian) filter efficiently. The resulting algorithm reaches the performance of GPB2 with less Kalman filters.
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Xu, Liou. "A MARKOV TRANSITION MODEL TO DEMENTIA WITH DEATH AS A COMPETING EVENT." UKnowledge, 2010. http://uknowledge.uky.edu/gradschool_diss/42.
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The research on multi-state Markov transition model is motivated by the nature of the longitudinal data from the Nun Study (Snowdon, 1997), and similar information on the BRAiNS cohort (Salazar, 2004). Our goal is to develop a flexible methodology for handling the categorical longitudinal responses and competing risks time-to-event that characterizes the features of the data for research on dementia. To do so, we treat the survival from death as a continuous variable rather than defining death as a competing absorbing state to dementia. We assume that within each subject the survival component and the Markov process are linked by a shared latent random effect, and moreover, these two pieces are conditionally independent given the random effect and their corresponding predictor variables. The problem of the dependence among observations made on the same subject (repeated measurements) is addressed by assuming a first order Markovian dependence structure.
A closed-form expression for the individual and thus overall conditional marginal likelihood function is derived, which we can evaluate numerically to produce the maximum likelihood estimates for the unknown parameters. This method can be implemented using standard statistical software such as SAS Proc Nlmixed©. We present the results of simulation studies designed to show how the model’s ability to accurately estimate the parameters can be affected by the distributional form of the survival term.
Then we focus on addressing the problem by accommodating the residual life time of the subject’s confounding in the nonhomogeneous chain. The convergence status of the chain is examined and the formulation of the absorption statistics is derived. We propose using the Delta method to estimate the variance terms for construction of confidence intervals. The results are illustrated with applications to the Nun Study data in details.
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Leuwattanachotinan, Charnchai. "Model fitting of a two-factor arbitrage-free model for the term structure of interest rates using Markov chain Monte Carlo." Thesis, Heriot-Watt University, 2011. http://hdl.handle.net/10399/2425.
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In this thesis we use Markov chain Monte Carlo (MCMC) simulation to calibrate a two-factor arbitrage-free model for the term structure of interest rates which is proposed by Cairns (2004a) based on the positive-interest framework (Flesaker and Hughston, 1996). The model is a time-homogeneous model driven by latent state variables which follow a two-dimensional Ornstein-Uhlenbeck process. A number of MCMC algorithms are developed and employed for estimating both model parameters and latent variables where simulated data are used in the first place in order to validate the algorithms and ensure that they can result in reasonable and reliable estimates before using UK market data. Once the posterior estimates are obtained, we next investigate goodness of fit of the model and eventually assess the impact of parameter uncertainty on the forecasting of yield curves in which the achieved MCMC output can be used directly. Additionally, the developed algorithm is also applied for estimating the two-factor Vasicek term structure model for comparison. We conclude that our algorithms work reasonably well for estimating the Cairns term structure model. The model is then fitted to UK Strips data, and it found to produce reasonable fits for medium- and long-term yields, but we also conclude that some improvement may be required for the short-end of the yield curves.
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Carlsson, Filip. "Can students' progress data be modeled using Markov chains?" Thesis, KTH, Matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254285.
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In this thesis a Markov chain model, which can be used for analysing students’ performance and their academic progress, is developed. Being able to evaluate students progress is useful for any educational system. It gives a better understanding of how students resonates and it can be used as support for important decisions and planning. Such a tool can be helpful for managers of the educational institution to establish a more optimal educational policy, which ensures better position in the educational market. To show that it is reasonable to use a Markov chain model for this purpose, a test for how well data fits such a model is created and used. The test shows that we cannot reject the hypothesis that the data can be fitted to a Markov chain model.I detta examensarbete utvecklas en Markov-kedjemodell, som kan användas för att analysera studenters prestation och akademiska framsteg. Att kunna utvärdera studenters väg genom studierna är användbart för alla utbildningssystem. Det ger en bättre förståelse för hur studenter resonerar och det kan användas som stöd för viktiga beslut och planering. Ett sådant verktyg kan vara till hjälp för utbildningsinstitutionens chefer att upprätta en mer optimal utbildningspolitik, vilket säkerställer en bättre ställning på utbildningsmarknaden. För att visa att det är rimligt att använda en Markov-kedjemodell för detta ändamål skapas och används ett test för hur väl data passar en sådan modell. Testet visar att vi inte kan avvisa hypotesen att data kan passa en Markov-kedjemodell.
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Fu, Jianlin. "A markov chain monte carlo method for inverse stochastic modeling and uncertainty assessment." Doctoral thesis, Universitat Politècnica de València, 2008. http://hdl.handle.net/10251/1969.
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Unlike the traditional two-stage methods, a conditional and inverse-conditional simulation approach may directly generate independent, identically distributed realizations to honor both static data and state data in one step. The Markov chain Monte Carlo (McMC) method was proved a powerful tool to perform such type of stochastic simulation. One of the main advantages of the McMC over the traditional sensitivity-based optimization methods to inverse problems is its power, flexibility and well-posedness in incorporating observation data from different sources. In this work, an improved version of the McMC method is presented to perform the stochastic simulation of reservoirs and aquifers in the framework of multi-Gaussian geostatistics.
First, a blocking scheme is proposed to overcome the limitations of the classic single-component Metropolis-Hastings-type McMC. One of the main characteristics of the blocking McMC (BMcMC) scheme is that, depending on the inconsistence between the prior model and the reality, it can preserve the prior spatial structure and statistics as users specified. At the same time, it improves the mixing of the Markov chain and hence enhances the computational efficiency of the McMC. Furthermore, the exploration ability and the mixing speed of McMC are efficiently improved by coupling the multiscale proposals, i.e., the coupled multiscale McMC method. In order to make the BMcMC method capable of dealing with the high-dimensional cases, a multi-scale scheme is introduced to accelerate the computation of the likelihood which greatly improves the computational efficiency of the McMC due to the fact that most of the computational efforts are spent on the forward simulations. To this end, a flexible-grid full-tensor finite-difference simulator, which is widely compatible with the outputs from various upscaling subroutines, is developed to solve the flow equations and a constant-displacement random-walk
particle-tracking method, which enhances the comFu, J. (2008). A markov chain monte carlo method for inverse stochastic modeling and uncertainty assessment [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/1969
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Hussain, Arshad. "Stochastic modeling of rainfall processes: a Markov chain - mixed exponential model for rainfalls in different climatic conditions." Thesis, McGill University, 2008. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=18710.
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Watershed models simulating the physical process of runoff usually require daily or sub-daily rainfall time series data as input. However, even when rainfall records are available, they contain only limited and finite information regarding the historical rainfall pattern to adequately assess the response and reliability of a water resource system. This study is therefore concerned with the development of a stochastic rainfall model that can reliably generate many sequences of synthetic rainfall time series' that have similar properties to those of the observed data. The 'MCME' model developed is based on a combination of the rainfall occurrence (described using a Markov Chain process) and the distribution of rainfall amounts on wet days (represented by the Mixed-Exponential probability function). Various optimization methods were tested to best calibrate the model's parameters and the model was then applied to daily rainfall data from 3 different regions across the globe (Dorval, Quebec, Sooke Reservoir in British Columbia and Roxas City in the Philippines) to assess the accuracy and suitability of the model for daily rainfall simulation. The feasibility of the MCME model was also assessed using hourly rainfall data available at Dorval Airport in Quebec (Canada). In general, it was found that the proposed MCME model was able to adequately describe various statistical and physical properties of the daily and hourly rainfall processes considered. In addition, an innovative approach was proposed to combine the estimation of daily annual maximum precipitations (AMPs) by the MCME with those by the downscaled Global Circulation Models (GCMs). The combined model was found to able to provide AMP estimates that were comparable to the observed values at a local site. In particular, the suggested linkage between the MCME and downscaled-GCM outputs would be useful for various climate change impact studies involving rainfall extremes.La précipitation est souvent considérée comme la composante d'entrée principale pour les modèles de simulation de ruissellement. Toutefois, même si les donnés de précipitation sont disponibles, ces données ne contiennent qu'une quantité d'information limitée concernant la variabilité de précipitation dans le passé. La présente étude a alors pour objet d'élaborer un modèle stochastique de précipitation qui est capable de générer plusieurs séries synthétiques de précipitation ayant les mêmes propriétés statistiques et physiques que les données historiques. Le modèle MCME proposé dans cette étude consiste a une combinaison de la composante d'apparition de pluie (représentée par la chaîne de Markov) et la composante de répartition de quantité de précipitation (représentée par la loi exponentielle mixte). L'évaluation de la faisabilité et de la précision de ce modèle a été effectuée en utilisant les données de précipitations journalières disponibles en trois sites situés dans trois régions différentes du monde et en utilisant plusieurs méthodes de calibration par les techniques d'optimisation locale et globale. La faisabilité du modèle MCME a été également évaluée avec les données de précipitation horaire disponibles a l'aéroport de Dorval au Québec (Canada). En général on a trouvé que le modèle MCME est capable de décrire adéquatement diverses propriétés statistiques et physiques des processus de précipitations journalier et horaire considérés. En plus, une approche innovatrice a été suggérée pour combiner l'estimation des précipitations annuelles maximales par le modèles MCME avec celles fournies par la mise en échelle des modèles de circulation globale (GCM). On a trouvé que les modèles combinés sont capable du calculer les précipitations annuelles maximales qui sont comparables aux valeurs observées en un site donné. En particulier la connection entre le modèle MC
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Muhammad, Usman Rehan. "Probabilistic and Risk Analysis ofChannel Allocation Schemes,based on Markov Chain Model,used for Next Generation Networks." Thesis, KTH, Tillämpad maskinteknik (KTH Södertälje), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-143956.
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Frühwirth-Schnatter, Sylvia. "Bayesian Model Discrimination and Bayes Factors for Normal Linear State Space Models." Department of Statistics and Mathematics, WU Vienna University of Economics and Business, 1993. http://epub.wu.ac.at/108/1/document.pdf.
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It is suggested to discriminate between different state space models for a given time series by means of a Bayesian approach which chooses the model that minimizes the expected loss. Practical implementation of this procedures requires a fully Bayesian analysis for both the state vector and the unknown hyperparameters which is carried out by Markov chain Monte Carlo methods. Application to some non-standard situations such as testing hypotheses on the boundary of the parameter space, discriminating non-nested models and discrimination of more than two models is discussed in detail. (author's abstract)Series: Forschungsberichte / Institut für Statistik
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Florence, Lindsay Walker. "Skill Evaluation in Women's Volleyball." Diss., CLICK HERE for online access, 2008. http://contentdm.lib.byu.edu/ETD/image/etd2286.pdf.
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Trávníček, Jan. "Tvorba spolehlivostních modelů pro pokročilé číslicové systémy." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2013. http://www.nusl.cz/ntk/nusl-236226.
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This thesis deals with the systems reliability. At First, there is discussed the concept of reliability itself and its indicators, which can specifically express reliability. The second chapter describes the different kinds of reliability models for simple and complex systems. It further describes the basic methods for construction of reliability models. The fourth chapter is devoted to a very important Markov models. Markov models are very powerful and complex model for calculating the reliability of advanced systems. Their suitability is explained here for recovered systems, which may contain absorption states. The next chapter describes the standby redundancy. Discusses the advantages and disadvantages of static, dynamic and hybrid standby. There is described the influence of different load levels on the service life. The sixth chapter is devoted to the implementation, description of the application and description of the input file in XML format. There are discussed the results obtaining in experimental calculations.
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Park, Pangun. "Modeling, Analysis and Design of Wireless Sensor Network Protocols." Doctoral thesis, KTH, Reglerteknik, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-29821.
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Wireless sensor networks (WSNs) have a tremendous potential to improve the efficiencyof many systems, for instance, in building automation and process control.Unfortunately, the current technology does not offer guaranteed energy efficiencyand reliability for closed-loop stability. The main contribution of this thesis is toprovide a modeling, analysis, and design framework for WSN protocols used in controlapplications. The protocols are designed to minimize the energy consumption ofthe network, while meeting reliability and delay requirements from the applicationlayer. The design relies on the analytical modeling of the protocol behavior.First, modeling of the slotted random access scheme of the IEEE 802.15.4medium access control (MAC) is investigated. For this protocol, which is commonlyemployed in WSN applications, a Markov chain model is used to derive theanalytical expressions of reliability, delay, and energy consumption. By using thismodel, an adaptive IEEE 802.15.4 MAC protocol is proposed. The protocol designis based on a constrained optimization problem where the objective function is theenergy consumption of the network, subject to constraints on reliability and packetdelay. The protocol is implemented and experimentally evaluated on a test-bed. Experimentalresults show that the proposed algorithm satisfies reliability and delayrequirements while ensuring a longer lifetime of the network under both stationaryand transient network conditions.Second, modeling and analysis of a hybrid IEEE 802.15.4 MAC combining theadvantages of a random access with contention with a time division multiple access(TDMA) without contention are presented. A Markov chain is used to model thestochastic behavior of random access and the deterministic behavior of TDMA.The model is validated by both theoretical analysis and Monte Carlo simulations.Using this new model, the network performance in terms of reliability, averagepacket delay, average queueing delay, and throughput is evaluated. It is shown thatthe probability density function of the number of received packets per superframefollows a Poisson distribution. Furthermore, it is determined under which conditionsthe time slot allocation mechanism of the IEEE 802.15.4 MAC is stable.Third, a new protocol for control applications, denoted Breath, is proposedwhere sensor nodes transmit information via multi-hop routing to a sink node. Theprotocol is based on the modeling of randomized routing, MAC, and duty-cycling.Analytical and experimental results show that Breath meets reliability and delayrequirements while exhibiting a nearly uniform distribution of the work load. TheBreath protocol has been implemented and experimentally evaluated on a test-bed.Finally, it is shown how the proposed WSN protocols can be used in controlapplications. A co-design between communication and control application layers isstudied by considering a constrained optimization problem, for which the objectivefunction is the energy consumption of the network and the constraints are thereliability and delay derived from the control cost. It is shown that the optimaltraffic load when either the communication throughput or control cost are optimizedis similar.QC 20110217
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Tüchler, Regina. "Bayesian Variable Selection for Logistic Models Using Auxiliary Mixture Sampling." Department of Statistics and Mathematics, WU Vienna University of Economics and Business, 2006. http://epub.wu.ac.at/984/1/document.pdf.
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The paper presents an Markov Chain Monte Carlo algorithm for both variable and covariance selection in the context of logistic mixed effects models. This algorithm allows us to sample solely from standard densities, with no additional tuning being needed. We apply a stochastic search variable approach to select explanatory variables as well as to determine the structure of the random effects covariance matrix. For logistic mixed effects models prior determination of explanatory variables and random effects is no longer prerequisite since the definite structure is chosen in a data-driven manner in the course of the modeling procedure. As an illustration two real-data examples from finance and tourism studies are given. (author's abstract)Series: Research Report Series / Department of Statistics and Mathematics
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Mushtaq, Aleem. "An integrated approach to feature compensation combining particle filters and Hidden Markov Models for robust speech recognition." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/48982.
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The performance of automatic speech recognition systems often degrades in adverse conditions where there is a mismatch between training and testing conditions. This is true for most modern systems which employ Hidden Markov Models (HMMs) to decode speech utterances. One strategy is to map the distorted features back to clean speech features that correspond well to the features used for training of HMMs. This can be achieved by treating the noisy speech as the distorted version of the clean speech of interest. Under this framework, we can track and consequently extract the underlying clean speech from the noisy signal and use this derived signal to perform utterance recognition. Particle filter is a versatile tracking technique that can be used where often conventional techniques such as Kalman filter fall short. We propose a particle filters based algorithm to compensate the corrupted features according to an additive noise model incorporating both the statistics from clean speech HMMs and observed background noise to map noisy features back to clean speech features. Instead of using specific knowledge at the model and state levels from HMMs which is hard to estimate, we pool model states into clusters as side information. Since each cluster encompasses more statistics when compared to the original HMM states, there is a higher possibility that the newly formed probability density function at the cluster level can cover the underlying speech variation to generate appropriate particle filter samples for feature compensation. Additionally, a dynamic joint tracking framework to monitor the clean speech signal and noise simultaneously is also introduced to obtain good noise statistics. In this approach, the information available from clean speech tracking can be effectively used for noise estimation. The availability of dynamic noise information can enhance the robustness of the algorithm in case of large fluctuations in noise parameters within an utterance. Testing the proposed PF-based compensation scheme on the Aurora 2 connected digit recognition task, we achieve an error reduction of 12.15% from the best multi-condition trained models using this integrated PF-HMM framework to estimate the cluster-based HMM state sequence information. Finally, we extended the PFC framework and evaluated it on a large-vocabulary recognition task, and showed that PFC works well for large-vocabulary systems also.
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Herrick, Robert L. "Using Markov Chain Monte Carlo Models to Estimate the Severity, Duration and Cost of a Salmonellosis Outbreak of Known Size." University of Cincinnati / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1227284690.
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Song, Joon Jin. "Bayesian multivariate spatial models and their applications." Texas A&M University, 2004. http://hdl.handle.net/1969.1/1122.
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Univariate hierarchical Bayes models are being vigorously researched for use in disease mapping, engineering, geology, and ecology. This dissertation shows how the models can also be used to build modelbased risk maps for areabased roadway traffic crashes. Countylevel vehicle crash records and roadway data from Texas are used to illustrate the method. A potential extension that uses univariate hierarchical models to develop networkbased risk maps is also discussed.
Several Bayesian multivariate spatial models for estimating the traffic crash rates from different types of crashes simultaneously are then developed. The specific class of spatial models considered is conditional autoregressive (CAR) model. The univariate CAR model is generalized for several multivariate cases. A general theorem for each case is provided to ensure that the posterior distribution is proper under improper and flat prior. The performance of various multivariate spatial models is compared using a Bayesian information criterion. The Markov chain Monte Carlo (MCMC) computational techniques are used for the model parameter estimation and statistical inference. These models are illustrated and compared again with the Texas crash data.
There are many directions in which this study can be extended. This dissertation concludes with a short summary of this research and recommends several promising extensions.
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Kerl, John R. "Critical behavior for the model of random spatial permutations." Diss., The University of Arizona, 2010. http://hdl.handle.net/10150/193647.
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We examine a phase transition in a model of random spatial permutations which originates in a study of the interacting Bose gas. Permutations are weighted according to point positions; the low-temperature onset of the appearance of arbitrarily long cycles is connected to the phase transition of Bose-Einstein condensates. In our simplified model, point positions are held fixed on the fully occupied cubic lattice and interactions are expressed as Ewens-type weights on cycle lengths of permutations. The critical temperature of the transition to long cycles depends on an interaction-strength parameter α. For weak interactions, the shift in critical temperature is expected to be linear in α with constant of linearity c. Using Markov chain Monte Carlo methods and finite-size scaling, we find c = 0.618 ± 0.086. This finding matches a similar analytical result of Ueltschi and Betz. We also examine the mean longest cycle length as a fraction of the number of sites in long cycles, recovering an earlier result of Shepp and Lloyd for non-spatial permutations. The plan of this paper is as follows. We begin with a non-technical discussion of the historical context of the project, along with a mention of alternative approaches. Relevant previous works are cited, thus annotating the bibliography. The random-cycle approach to the BEC problem requires a model of spatial permutations. This model it is of its own probabilistic interest; it is developed mathematically, without reference to the Bose gas. Our Markov-chain Monte Carlo algorithms for sampling from the random-cycle distribution - the swap-only, swap-and-reverse, band-update, and worm algorithms - are presented, compared, and contrasted. Finite-size scaling techniques are used to obtain information about infinite-volume quantities from finite-volume computational data.
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Loza, Reyes Elisa. "Classification of phylogenetic data via Bayesian mixture modelling." Thesis, University of Bath, 2010. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.519916.
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Conventional probabilistic models for phylogenetic inference assume that an evolutionary tree,andasinglesetofbranchlengthsandstochasticprocessofDNA evolutionare sufficient to characterise the generating process across an entire DNA alignment. Unfortunately such a simplistic, homogeneous formulation may be a poor description of reality when the data arise from heterogeneous processes. A well-known example is when sites evolve at heterogeneous rates. This thesis is a contribution to the modelling and understanding of heterogeneityin phylogenetic data. Weproposea methodfor the classificationof DNA sites based on Bayesian mixture modelling. Our method not only accounts for heterogeneous data but also identifies the underlying classes and enables their interpretation. We also introduce novel MCMC methodology with the same, or greater, estimation performance than existing algorithms but with lower computational cost. We find that our mixture model can successfully detect evolutionary heterogeneity and demonstrate its direct relevance by applying it to real DNA data. One of these applications is the analysis of sixteen strains of one of the bacterial species that cause Lyme disease. Results from that analysis have helped understanding the evolutionary paths of these bacterial strains and, therefore, the dynamics of the spread of Lyme disease. Our method is discussed in the context of DNA but it may be extendedto othertypesof molecular data. Moreover,the classification scheme thatwe propose is evidence of the breadth of application of mixture modelling and a step forwards in the search for more realistic models of theprocesses that underlie phylogenetic data.
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Sonksen, Michael David. "Bayesian Model Diagnostics and Reference Priors for Constrained Rate Models of Count Data." The Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1312909127.
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Masoumi, Samira. "Model Discrimination Using Markov Chain Monte Carlo Methods." Thesis, 2013. http://hdl.handle.net/10012/7465.
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Model discrimination deals with situations where there are several candidate models available to represent a system. The objective is to find the “best” model among rival models with respect to prediction of system behavior. Empirical and mechanistic models are two important categories of models. Mechanistic models are developed based on physical mechanisms. These types of models can be applied for prediction purposes, but they are also developed to gain improved understanding of the underlying physical mechanism or to estimate physico-chemical parameters of interest. When model discrimination is applied to mechanistic models, the main goal is typically to determine the “correct” underlying physical mechanism. This study focuses on mechanistic models and presents a model discrimination procedure which is applicable to mechanistic models for the purpose of studying the underlying physical mechanism.
Obtaining the data needed from the real system is one of the challenges particularly in applications where experiments are expensive or time consuming. Therefore, it is beneficial to get the maximum information possible from the real system using the least possible number of experiments.
In this research a new approach to model discrimination is presented that takes advantage of Monte Carlo (MC) methods. It combines a design of experiments (DOE) method with an adaptation of MC model selection methods to obtain a sequential Bayesian Markov Chain Monte Carlo model discrimination framework which is general and usable for a wide range of model discrimination problems.
The procedure has been applied to chemical engineering case studies and the promising results have been discussed. Four case studies, order of reaction, rate of FeIII formation, copolymerization, and RAFT polymerization, are presented in this study.
The first three benchmark problems allowed us to refine the proposed approach. Moreover, applying the Sequential Bayesian Monte Carlo model discrimination framework in the RAFT problem made a contribution to the polymer community by recommending analysis an approach to selecting the correct mechanism.
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Ming-Chieh, Lin, and 林銘捷. "The Markov Chain Model for Customer Lifetime Value." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/72828005087883748717.
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碩士南台科技大學
行銷與流通管理系
96
In Costumer Relationship Management (CRM) ,Customer lifetime values (CLV) is rapidly gaining acceptance as an authority to allocate firm’s marketing resource. Any successful customer relationship Management need a database to save customers’ historical purchase data for tracking individual customer’s purchase behavior over time. Database Marketing is a key for firms to implement one-to-one marketing. The CLV is the present value of all future profits generated from this customer. In order to use CLV for allocating marketing resources, firms need to calculate individual customer value. CRM is the process of analyzing a firm’s customer interactions in order to enhance the CLV to the firm. Using CLV to allocate marketing resources assumes that the CLV can be estimated accurately. The efficiency and effectiveness of the CLV is most importation tasks in firm operation. Therefore, this study presents a Markov chain model to calculate CLV. The framework explicitly considers Recency, Frequency and Monetary value (RFM) of customer purchase in transition matrix of Markov chain model. Markov chain model is concerned with the calculation of CLV. A numerical example will show the performance of Markov chain model for CLV. Hence, it could actually decide when to close customer relationship which is count for nothing by evaluating individual customer value.
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Yang, Chung-Han, and 楊仲涵. "Applying Markov chain model to forecast short-term wind speed." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/37908165537578491490.
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碩士國立勤益科技大學
工業工程與管理系
101
In recent years, global warming and energy crisis issues have become significant. Pollution-free andlower cost of wind power is becoming the global mainstream. Wind speed is the most important factor to impact the generation of wind power. Building the wind speed model for simulation and predictions becomes an important issue. This studyinvestigatesdifferentMarkov chain modelsbased onclassified states and wind speed conversion for wind turbines in Taiwan.We further develop the first, second and third-order Markov chain to compare the simulation results of the statisticalparameters,error index and autocorrelation function to identify the most suitable Markov model. The results show that the speed simulated by Markov chain, which can effectively preserve the original speed of mean and standard deviation, and the second-order Markov chain is better than first-order and third-order. The results of this study cansimulate the wind speed model accurately so that turbine procurement, construction and distribution of electricity can be greatly benefited.
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Huang, Chien-Chung, and 黃建中. "Development of Freeway Pavement Performance Prediction Model Using Markov Chain." Thesis, 1997. http://ndltd.ncl.edu.tw/handle/64749159238631643023.
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碩士淡江大學
土木工程學系
85
To maximize the benefits of pavement management, a reliable method for pavement performance prediction is extremely important. The objectives of this study were to investigate performance-prediction related literature, collect field data, suggest applicable method, and finally develop freeway pavement performance prediction model in Taiwan. There are many factors that affect the rate of deterioration, thus the behavior of pavements varies from one to another. The preliminary analysis of the data collected concluded that the regression analysis (deterministic model) can not provide applicable model because the districts do not have complete database that can provide adequate data for each section in the network. On the other hand, the Markov chain approach(probabilistic model) could portray the rate of deterioration as uncertain, random behavior. Consequently, efforts was made to develop the prediction models using Markov Chain. Two models were developed from the data of two districts of the Taiwan Area National Freeway Bureau. The verification of these modelsshow that they could adequately portray the pavement behavior. Furthermore, these models could straightly be established, employed, renewed, and applicable to the pavement management system.
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"Finite element model updating using Markov Chain Monte Carlo techniques." Thesis, 2015. http://hdl.handle.net/10210/14668.
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D.Ing.Monte Carlo (HMC), to update two different structural beam models. The practical issues and the feasibility of the three algorithms to such problems are addressed, and adjustments are made to make them more effective and efficient for solving model updating problems. As a result, both M-H and HMC techniques are found to perform better than the SS algorithm when a Cantilever beam was updated. Also, the HMC method gives better results than M-H and SS when an unsymmetrical H-shaped beam structure was updated. In both examples, the HMC converges faster than M-H and SS algorithms ...
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yu-ping, Hong, and 洪雨平. "Applying RFM model and Markov Chain in Customer Value Analysis." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/39400770841878262471.
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碩士國立臺灣大學
商學研究所
90
With development of information technology, relationship between enterprises and customers gets complex and prompt. Amid keen competition, resources allocation decision among customers become significant. To allocate resources efficiently and cut down waste of marketing budget, customer value analysis turns to be an important tool. In this thesis, Markov chain and RFM model are integrated to calculate customer lifetime value（CLV）. Then customer lifetime value is used to allocate marketing budget and solve the problem. The model integrates RFM model, Markov chain and discounting method to derive profit contribution of customers in every purchasing situation. In the first step RFM model is used to define customers purchasing state while transition matrix is designed to describe probabilities among purchasing states. With revenue and cost data, profit contribution of each period can be calculated. After discounted, profit contribution is accumulated to be customer lifetime value. In order to compare the original method and real data, three months of transaction data were used. Finally, customer lifetime value can be used to indicate from the empirical case study, we have the following conclusions: 1.The new model performs better than the traditional customer lifetime value estimate method, especially when the retaining rate is low. 2.Customer purchasing behavior and probability are estimated by using Markov Chain. Customer lifetime value can be used to resource allocation.