Markov Models Research Papers - Academia.edu (original) (raw)

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The phenomena of machine failures, defects, multiple rework loops, etc., results in much difficulty in modeling rework systems, and therefore the performance analysis of such systems has been investigated limitedly in the past. We propose... more

The phenomena of machine failures, defects, multiple rework loops, etc., results in much difficulty in modeling rework systems, and therefore the performance analysis of such systems has been investigated limitedly in the past. We propose an analytical method for the performance evaluation of rework systems with unreliable machines and finite buffers. To characterize the rework flow in the system, a new 3M1B (three-machine and one-buffer) Markov model is first presented. Unlike previous models, it is capable of representing multiple rework loops, and the rework fraction of each loop is calculated based on the quality of material flow in the system. A decomposition method is then developed for multistage rework systems using the proposed 3M1B model as one of the building blocks. The experimental results demonstrate that the decomposition method provides accurate estimates of performance measures such as throughput and Work-In-Process (WIP). We have applied this method to several problems, such as the determination of the optimal inspection location and the identification of bottleneck machines in rework systems.► We propose an analytical model for manufacturing systems with multiple rework loops. ► We solve problems such as inspection allocation and bottleneck identification. ► Bottlenecks of rework systems migrate differently compared to systems without rework. ► We propose a continuous improvement strategy in the paper.

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  • Engineering, Markov Models, Performance Analysis, Performance Evaluation
• • by Costas Katsoufis
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  • Distributed Computing, Computational Modeling, Monte Carlo, Technology Management
• • by William Knottenbelt
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  • Distributed Computing, Distributed Shared Memory System, Markov Models, Communication System
• • by Purnendu Bikash
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  • Information Systems, Statistical Analysis, Speech Production, Automatic Speech Recognition
• • by Muhammad Abuelma'atti and +1
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  • Engineering, Education, Markov Processes, Probability

In this paper we present a method for transient analysis of availability and survivability of a system with the identical components and identical repairmen. The considered system is supposed to consist of series of k-out-of-n or parallel... more

In this paper we present a method for transient analysis of availability and survivability of a system with the identical components and identical repairmen. The considered system is supposed to consist of series of k-out-of-n or parallel components. We employed the Markov models, eigen vectors and eigenvalues for analyzing the transient availability and survivability of the system. The method is implemented through an algorithm which is tested in MATLAB programming environment. The new method enjoys a stronger mathematical foundation and more flexibility for analyzing the transient availability and survivability of the system.

• • by farhad tari
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  • Applied Mathematics, Methodology, Algorithm, Multidisciplinary
• • by Kishor S Trivedi
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  • Applied Mathematics, Computer Science, Statistics, Markov Models

The parameters of a discrete stationary Markov model are transition probabilities between states. Traditionally, data consist in sequences of observed states for a given number of individuals over the whole observation period. In such a... more

The parameters of a discrete stationary Markov model are transition probabilities between states. Traditionally, data consist in sequences of observed states for a given number of individuals over the whole observation period. In such a case, the estimation of transition probabilities is straightforwardly made by counting one-step moves from a given state to another. In many real-life problems, however, the inference is much more difficult as state sequences are not fully observed, namely the state of each individual is known only for some given values of the time variable. A review of the problem is given, focusing on Monte Carlo Markov Chain (MCMC) algorithms to perform Bayesian inference and evaluate posterior distributions of the transition probabilities in this missing-data framework. Leaning on the dependence between the rows of the transition matrix, an adaptive MCMC mechanism accelerating the classical Metropolis-Hastings algorithm is then proposed and empirically studied.

• • by Alberto Pasanisi
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  • Econometrics, Statistics, Statistical Computing, Markov Models
• • by Kostas Karpouzis
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  • Computer Vision, Gesture Recognition, Image segmentation, Markov Models

The continuous growth in the size and use of the World Wide Web imposes new methods of design and development of on-line information services. The need for predicting the users ’ needs in order to improve the usability and user retention... more

The continuous growth in the size and use of the World Wide Web imposes new methods of design and development of on-line information services. The need for predicting the users ’ needs in order to improve the usability and user retention of a web site is more than evident and can be addressed by personalizing it. Recommendation algorithms aim at proposing “next ” pages to users based on their current visit and the past users’ navigational patterns. In the vast majority of related algorithms, however, only the usage data are used to produce recommendations, disregarding the structural properties of the web graph. Thus important – in terms of PageRank authority score – pages may be underrated. In this work we present UPR, a PageRank-style algorithm which combines usage data and link analysis techniques for assigning probabilities to the web pages based on their importance in the web site’s navigational graph. We propose the application of a localized version of UPR (l-UPR) to personal...

• • by Magdalini Eirinaki
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  • Information Systems, Computer Science, Algorithms, Distributed Computing
• • by otoniel lopez
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  • Video Compression, Markov Models, Performance Evaluation, Wireless Network
• • by Magdalini Eirinaki
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  • Information Systems, Distributed Computing, Web Personalization, Markov Models
• • by Ermanno Pitacco
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  • Markov Models
• • by Refik Soyer
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  • Econometrics, Statistics, Markov Models, Bayesian Inference
• • by Ben Pelzer
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  • Time Series, Political Science, Intellectual Disability, Markov Models
• • by Avi Harel
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  • Computer Science, Usability, Data Analysis, Timing Analysis
• • by Tadeusz Czachorski
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  • Computer Networks, Markov Processes, Queueing theory, Queuing Theory
• • by Lora Billings and +1
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  • Markov Models, Computer Virus, Computer Viruses, Mathematical Sciences
• • by Magdalini Eirinaki
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  • Web Personalization, Markov Models, Data Structure, Link analysis
• • by Baris Tan
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  • Applied Mathematics, Markov Models, Production Systems, Throughput
• • by Gael Varoquaux and +1
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  • Algorithms, Magnetic Resonance Imaging, Markov Models, Brain Mapping

Aviation safety system have been analyzed in two ways - economically and technologically, to determine the most resource-consuming life circle phases. A model to analyze the safety system in the whole life cycle is presented. The model... more

Aviation safety system have been analyzed in two ways - economically and technologically, to determine the most resource-consuming life circle phases. A model to analyze the safety system in the whole life cycle is presented. The model was used imposing current specified cost and technological limits of existing safety systems and requirements to modem aviation safety systems. The concept of dynamic safety system has been discussed to enable handling of risk limits along the flight phases. Copyright (C) 2000 IFAC.

• • by Igor V Schagaev
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  • Economics, Risk Management, Aviation, Markov Models
• • by Stefanos Kollias
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  • Markov Processes, Gesture Recognition, Markov Models, Kohonen Self-Organizing Feature Maps (SOFMs)
• • by D. Hogg
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  • Cognitive Science, Sign Language, Computer Animation, Image Recognition (Computer Vision)

Land-cover and land-use (LCLU) change was quantified for the last 35 years within and in the vicinity of a fast growing city in Mexico, using rectified aerial photographs and geographic information systems (GIS). LCLU change was projected... more

Land-cover and land-use (LCLU) change was quantified for the last 35 years within and in the vicinity of a fast growing city in Mexico, using rectified aerial photographs and geographic information systems (GIS). LCLU change was projected for the next 20 years using Markov chains ...

• • by Erna López
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  • Engineering, Developing Countries, Landscape, Urban Landscape
• • by Denise Kirschner
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  • Systems Biology, Communicable Diseases, System Biology, Theoretical biology
• • by Massimo Pietropaolo
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  • Autoimmunity, Markov Models, Humans, Insulin
• • by Joseph R Cavallaro
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  • Fuzzy Logic, Markov Processes, Reliability Theory, Reliability Engineering
• • by Arvind Narayanan
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  • Natural Language Processing, Computer Security, Markov Models, Cryptanalysis
• • by Suresh Bhargava
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  • Chemical Engineering, Catalysts, Markov Models, Gold nanoparticle
• • by Stanley Gershwin
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  • Engineering, Decomposition, Evaluation, Markov Models
• • by Kishor S Trivedi
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  • Information Systems, Marketing, Performance, Software Architecture

With the rapid growth of information on the Web and increase of users who are daily visiting the web sites, presenting information proportionate to requirements of users who are visiting a special website so that they could find their... more

With the rapid growth of information on the Web and increase of users who are daily visiting the web sites, presenting information proportionate to requirements of users who are visiting a special website so that they could find their desired information would be essential. Therefore, analyzing browsing behavior of web users and modeling this behavior has particular importance. The aim of recommender systems is guiding users to find their favorite resources and meet their needs, using the information obtained from the previous users’ interactions. In this paper, to predict the web pages with high precision, a hybrid algorithm of clustering technique, All-K th-Order Markov model, and neural network are presented. For this purpose, in order to model users’ movement behavior, after clustering those with the same interests, the sequential patterns are extracted on users’ sessions of each cluster using all-4th-order Markov model. Next, in the step of pages recommendation to a current user, which is performed in an online state, first, a current user session is assigned to a cluster using neural network. Then Markov model created on the cluster which has the nearest match to the current session, is applied and a sequence of pages, which the users are interested to view, is included in the list of recommendation. The implementation results demonstrate that the proposed algorithm has higher precision and recall comparing to other recommender systems.

• • by ACSIJ Journal and +1
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  • Neural Networks, Markov Models, Clustering
• • by Alejandro Betancourt
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  • Computer Science, Game Theory, Markov Models, Power allocation

The effectiveness of SPICE in calculating probabilities, reliability, steady-state availability, and mean time to failure of repairable systems described by Markov models is demonstrated. Two examples are presented. The first example is a... more

The effectiveness of SPICE in calculating probabilities, reliability, steady-state availability, and mean time to failure of repairable systems described by Markov models is demonstrated. Two examples are presented. The first example is a two-unit, warm standby ...

• • by ISA QAMBER
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  • Engineering, Computer Science, Education, Markov Processes
• • by Manuel Mendoza
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  • Engineering, Developing Countries, Landscape, Urban Landscape
• • by Moustafa Youssef
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  • Distributed Computing, Markov Processes, Conditional Random Fields, Mobile Computing
• • by Daniel Weld
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  • Web Mining, Personalization, Markov Models, Web Navigation
• • by Günther Palm
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  • Machine Learning, Markov Models, Layered Architecture
• • by Edmundo de Souza e Silva and +1
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  • Markov Models, Performance Evaluation, Transient analysis
• • by Asli Celikyilmaz
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  • Markov Models
• • by Annalisa Bracco
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  • Statistics, Design, Turbulence, Reproduction
• • by Yongxin Cao
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  • Engineering, Markov Models, Performance Analysis, Performance Evaluation
• • by Paul Miller Gamble
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  • Econometrics, Statistics, Monte Carlo Simulation, Breast Cancer
• • by Kishor S Trivedi
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  • Computational Modeling, Iterative Methods, Markov Processes, Petri Nets
• • by Marie Cottrell
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  • Engineering, Data Mining, Data Analysis, Neural Network

High accuracy sequence classification often re- quires the use of higher order Markov models (MMs). However, the number of MM parameters increases exponentially with the range of direct dependencies between sequence elements, thereby... more

High accuracy sequence classification often re- quires the use of higher order Markov models (MMs). However, the number of MM parameters increases exponentially with the range of direct dependencies between sequence elements, thereby increasing the risk of overfitting when the data set is limited in size. We present abstraction augmented Markov models (AAMMs) that effectively reduce the number of nu- meric parameters of kth order MMs by successively grouping strings of length k (i.e., k-grams) into abstraction hierarchies. We evaluate AAMMs on three protein subcellular localization prediction tasks. The results of our experiments show that abstraction makes it possible to construct predictive models that use significantly smaller number of features (by one to three orders of magnitude) as compared to MMs. AAMMs are competitive with and, in some cases, significantly outperform MMs. Moreover, the results show that AAMMs often perform significantly better than variable order Markov models, such as decomposed context tree weighting, prediction by partial match, and probabilistic suffix trees.

• • by Vasant G Honavar
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  • Machine Learning, Data Mining, Probabilistic Markov Modeling, Abstraction

Markov models have been widely used for modelling users' navigational behaviour in the Web graph, using the transitional probabilities between web pages, as recorded in the web logs. The recorded users ' navigation is used to... more

Markov models have been widely used for modelling users' navigational behaviour in the Web graph, using the transitional probabilities between web pages, as recorded in the web logs. The recorded users ' navigation is used to extract popular web paths and predict current users ’ next steps. Such purely usage-based probabilistic models, however, present certain shortcomings. Since the prediction of users ' navigational behaviour is based solely on the usage data, structural properties of the Web graph are ignored. Thus important- in terms of pagerank authority score- paths may be underrated. In this paper we present a hybrid probabilistic predictive model extending the properties of Markov models by incorporating link analysis methods. More specifically, we propose the use of a PageRank-style algorithm for assigning prior probabilities to the web pages based on their importance in the web site's graph. We prove, through experimentation, that this approach results ...

• • by Magdalini Eirinaki
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  • Computer Science, Web Personalization, Markov Models, Data Structure

Abstract—Monterey Mirror is an interactive stochastic music generator based on Markov models, genetic algorithms, and power-law metrics for music information retrieval. It combines the predictive power of Markov models with the innovative... more

Abstract—Monterey Mirror is an interactive stochastic music generator based on Markov models, genetic algorithms, and power-law metrics for music information retrieval. It combines the predictive power of Markov models with the innovative power of genetic algorithms, using power-law metrics for fitness evaluation. These metrics have been developed and refined in a decade-long project, which explores music information retrieval based on Zipf’s law and related power laws. We describe the architecture of Monterey Mirror, which can generate musical responses based on aesthetic variations of user input. We also explore how such a system may be used as a musical metainstrument / environment in avant-garde music composition and performance projects.

• • by Bill Manaris
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  • Markov Models, Power Laws