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

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- Information Systems, Statistical Analysis, Speech Production, Automatic Speech Recognition
- by Joseph R Cavallaro
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- Fuzzy Logic, Markov Processes, Reliability Theory, Reliability Engineering
- by Ermanno Pitacco
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- Markov Models
- 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 Tony Robinson
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- Law, Automata, Speech Recognition, Multidisciplinary

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

- by Erna López
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- Engineering, Developing Countries, Landscape, Urban Landscape
- by Jeffrey B. Weiss
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- Statistics, Design, Turbulence, Reproduction
- by Stanley Gershwin
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- Engineering, Decomposition, Evaluation, 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 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 otoniel lopez
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- Video Compression, Markov Models, Performance Evaluation, Wireless Network
- by Kishor S Trivedi
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- Markov Models, Performance Analysis, Complex System, Performance Evaluation
- by Kostas Karpouzis
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- Computer Vision, Gesture Recognition, Image segmentation, Markov Models

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

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 Asli Celikyilmaz
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- Markov Models
- by James Bowring
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- Stochastic Process, Software Engineering, Machine Learning, Software Testing
- by Marie Cottrell
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- Engineering, Data Mining, Data Analysis, Neural Network
- 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 Baris Tan
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- Applied Mathematics, Markov Models, Production Systems, Throughput
- by Nicolas Bousquet and +1
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- Econometrics, Statistics, Statistical Computing, Markov Models
- by Annalisa Bracco
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- Statistics, Design, Turbulence, Reproduction
- by Gael Varoquaux and +1
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- Algorithms, Magnetic Resonance Imaging, Markov Models, Brain Mapping
- by Kishor S Trivedi
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- Information Systems, Marketing, Performance, Software Architecture
- by Muhammad Abuelma'atti and +1
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- Engineering, Education, Markov Processes, Probability
- by Paul Miller Gamble
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- Econometrics, Statistics, Monte Carlo Simulation, Breast Cancer
- by Jehan-Francois Paris
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- Computer Science, Software Reliability, Markov Models, Protocols
- by Magdalini Eirinaki
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- Web Personalization, Markov Models, Data Structure, Link analysis
- by Lora Billings and +1
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- Markov Models, Computer Virus, Computer Viruses, Mathematical Sciences
- by Massimo Pietropaolo
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- Autoimmunity, Markov Models, Humans, Insulin
- by James Bowring
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- Stochastic Process, Software Engineering, Machine Learning, Software Testing
- by Vincent Thomas and +1
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- Artificial Intelligence, Social Interaction, Interaction, Markov Models

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 Sachin Garg
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- Markov Models, Performance Analysis, Complex System, Performance Evaluation
- by Ethem Alpaydin
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- Information Systems, Selective Attention, Pattern Recognition, Face Recognition
- by Magdalini Eirinaki
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- Information Systems, Distributed Computing, Web Personalization, Markov Models
- by Günther Palm
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- Machine Learning, Markov Models, Layered Architecture
- by Changiz Eslahchi and +1
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- Algorithms, Prediction, Markov Models, Biological Sciences
- by Vasant G Honavar
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- Semi-supervised Learning, Markov Models, Expectation Maximization, Markov model
- by Fernanda Borges
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- Computational Chemistry, Markov Models, Humans, Markov chains
- by Gerardo Bocco
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- Engineering, Developing Countries, Landscape, Urban Landscape
- by Suresh Bhargava
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- Chemical Engineering, Catalysts, Markov Models, Gold nanoparticle
- by Benoit Liquet
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- Statistics, Markov Models, Interval Censoring, Cohort Study
- by farhad tari
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- Applied Mathematics, Methodology, Algorithm, Multidisciplinary

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 George Caridakis
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- Computer Vision, Gesture Recognition, Image segmentation, Markov Models
- by Carlos Calafate and +2
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- Markov Models, Mobile Ad Hoc Network, hidden Markov model, Wireless Ad hoc Networks