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

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- Information Systems, Statistical Analysis, Speech Production, Automatic Speech Recognition
- by Costas Katsoufis
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- Distributed Computing, Computational Modeling, Monte Carlo, Technology Management
- by George Karypis
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- Information Systems, Distributed Computing, Web Mining, Markov Models
- by Tony Robinson
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- Law, Automata, Speech Recognition, Multidisciplinary
- by Arvind Narayanan
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- Natural Language Processing, Computer Security, Markov Models, Cryptanalysis
- by Jeffrey B. Weiss
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- Statistics, Design, Turbulence, Reproduction
- by James Bowring
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- Stochastic Process, Software Engineering, Machine Learning, Software Testing
- by Daniel Weld
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- Web Mining, Personalization, Markov Models, Web Navigation
- by Sachin Garg
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- Markov Models, Performance Analysis, Complex System, Performance Evaluation
- by Magdalini Eirinaki
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- Web Personalization, Markov Models, Data Structure, Link analysis
- by Vibhu Sharma
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- Information Systems, Marketing, Performance, Software Architecture
- by Kishor S Trivedi
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- Computational Modeling, Iterative Methods, Markov Processes, Petri Nets
- by Gethin Norman
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- Cognitive Science, Numerical Method, Quantitative analysis, Markov Models
- by Fernanda Borges
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- Computational Chemistry, Markov Models, Humans, Markov chains
- by Massimo Pietropaolo
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- Autoimmunity, Markov Models, Humans, Insulin
- by Lora Billings and +1
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- Markov Models, Computer Virus, Computer Viruses, Mathematical Sciences
- by Alexandre Gramfort
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- Algorithms, Magnetic Resonance Imaging, Markov Models, Brain Mapping

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 Kishor S Trivedi
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- Applied Mathematics, Computer Science, Statistics, Markov Models
- by Benoit Liquet
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- Statistics, Markov Models, Interval Censoring, Cohort Study
- by Gethin Norman
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- Mechanical Engineering, Applied Mathematics, Model Checking, Statistical Analysis
- by Paul Miller Gamble
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- Econometrics, Statistics, Monte Carlo Simulation, Breast Cancer

Predicting mycobacterial sequences promoter of protein synthesis is important in the study of protein metabolism regulation. This goal is however considered a challenging computational biology task due to low inter-sequences homology. Consequently, a previous work based only on DNA sequence had to use a large input parameter set and multilayered feed-forward ANN architecture trained using the error-back-propagation algorithm to raise

- by alcides Perez Bello
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- Analytical Chemistry, Information Theory, Machine Learning, Entropy
- by Joseph R Cavallaro
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- Fuzzy Logic, Markov Processes, Reliability Theory, Reliability Engineering
- by Mahmoud El-Gamal
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- Monte Carlo Simulation, Economic Theory, Applied Economics, Time series analysis

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.

- by Velusamy Subramaniam
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- Engineering, Markov Models, Performance Analysis, Performance Evaluation
- by Stanley Gershwin
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- Engineering, Decomposition, Evaluation, Markov Models
- by Tamir Tuller
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- Genetics, Computational Biology, Molecular Evolution, Markov Processes
- by Magdalini Eirinaki
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- Information Systems, Distributed Computing, Web Personalization, Markov Models

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
- by Carlos Calafate
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- Video Compression, Markov Models, Performance Evaluation, Wireless Network
- by Baris Tan
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- Applied Mathematics, Markov Models, Production Systems, Throughput

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 Ashish Mathew
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- Linear Programming, Process Control, Markov Processes, Topology
- by Leigh McAlister
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- Marketing, Consumer Behavior, Comparative Study, Multidisciplinary
- by Shmuel Tomi Klein
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- Information Retrieval, Data Compression, Markov Processes, Databases
- by Mohammad Abdollahi Azgomi
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- Markov Processes, Probability, Markov Models, Security Model
- by Kostas Karpouzis
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- Computer Vision, Gesture Recognition, Image segmentation, Markov Models
- by Changiz Eslahchi and +1
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- Algorithms, Prediction, Markov Models, Biological Sciences
- by Muhammad Abuelma'atti and +1
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- Engineering, Education, Markov Processes, Probability
- by Günther Palm
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- Machine Learning, Markov Models, Layered Architecture
- by Jehan-Francois Paris
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- Computer Science, Software Reliability, Markov Models, Protocols
- by Carlos Calafate and +2
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- Markov Models, Mobile Ad Hoc Network, hidden Markov model, Wireless Ad hoc Networks

For the application of Markov state space models in modelling substations and switching procedures, i.e. a supply restoration procedure, it is crucial to determine all parameters that influence the supply interruption duration. The Markov model is chosen due to a possibility to clearly model all procedures and states during disturbances, which result in the supply interruption, according to [1, 2, 3]. In order to determine all the relevant parameters, the transmission network supply interruption reports from the year 2006 until the year 2010, as well as the supply interruption data in the transmission area Osijek from the year 2000 until the year 2010, were analysed. Based on this analysis, the supply restoration processes in the Croatian Transmission System Operator (TSO) and transmission area Osijek were determined. The Markov state space models application is possible only if probability density of failure and restoration is exponentially distributed [1-5]. Use of the Markov mode...

- by Damir Šljivac
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- Computer Science, Markov Models
- by ahmed khedra
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- Markov Models, DSS, Absorbing Markov Chain
- by Tadeusz Czachorski
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- Computer Networks, Markov Processes, Queueing theory, Queuing Theory