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Variable-order Markov model

Known as: Variable order Markov model, Variable order Markov models, Variable-Order Markov Models 
In stochastic processes, Variable-order Markov (VOM) models are an important class of models that extend the well known Markov chain models. In… 
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Related topics

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Artificial intelligenceBioinformaticsCodeData compression

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
2019
2019
Location Prediction based on Variable-order Markov Model with Time Feature and User’s Spatio-temporal Rule
Location-based service has been widely used in modern life. It brings a lot of convenience to our lives. Improving the accuracy… 
2018
2018
Event Correlation and Forecasting over Multivariate Streaming Sensor Data
Event management in sensor networks is a multidisciplinary field involving several steps across the processing chain. In this… 
2017
2017
Cooperation Between Top-Down and Low-Level Markov Chains for Generating Rock Drumming
Without heavy modification, the Markov chain is insufficient to handle the task of generating rock drum parts. This paper… 
2016
2016
Variable-order Markov Model for Community Detection
To better understand complex systems, researchers analyze interactions in physical or bio-logical systems with network models. To… 
2016
2016
How to Think Like a Data Scientist: Application of a Variable Order Markov Model to Indicators Management
The growing demand for specialists in analyzing large volumes of data has led to an emerging profile of knowledge managers known… 
2015
2015
A novel variable order Markov model based wireless service prediction algorithm with user similarity
More and more researches focus on wireless service prediction problems with the rapid development of wireless network technology… 
2012
2012
Efficient prediction model management in mobile systems
With the advent of affordable mobile devices such as smartphones and tablets, the vision of Pervasive Computing has made a big… 
Highly Cited
2009
Highly Cited
2009
Traffic-Known Urban Vehicular Route Prediction Based on Partial Mobility Patterns
Travel route analysis and prediction are essential for the success of many applications in Vehicular Ad-hoc Networks (VANETs… 
2008
2008
Gibbs sampling for parsimonious Markov models with latent variables
We propose a Bayesian model averaging approach for learning mixtures of parsimonious Markov models that is based on Gibbs… 
1996
1996
Parameterized Markov models for efficient compression of grayscale images
Lossless compression using finite context variable order Markov models generally achieves smaller compression ratios for small…