scholarly journals Google PageRank Algorithm: Markov Chain Model and Hidden Markov Model

2016 ◽  
Vol 138 (9) ◽  
pp. 9-13 ◽  
Author(s):  
Prerna Rai ◽  
Arvind Lal
Keyword(s):  
Markov Chain ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Markov Chain Model ◽  
Chain Model ◽  
Pagerank Algorithm

A Hidden Markov Model-Based Map-Matching Algorithm for Wheelchair Navigation

2009 ◽  
Vol 62 (3) ◽  
pp. 383-395 ◽  
Author(s):  
Ming Ren ◽  
Hassan A. Karimi
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Viterbi Algorithm ◽  
Hidden Markov ◽  
Markov Chain Model ◽  
Computational Time ◽  
Chain Model ◽  
Map Matching ◽  
Matching Algorithm ◽  
Matching Techniques

Application of map-matching techniques to GPS positions can provide accurate vehicle location information in challenging situations. The Hidden Markov Model (HMM) is a statistical model that is well known for providing solutions to temporal recognition applications such as text and speech recognition. This paper introduces a novel map-matching algorithm based on HMM for GPS-based wheelchair navigation. Given GPS positions, a hidden Markov chain model is established by using both geometric data and the topology of sidewalk segments. The map-matching algorithm employs the Viterbi algorithm to estimate correct sidewalk segments as hidden states in a HMM in order to match GPS trajectory on the corresponding segment sequence. The HMM-based map-matching algorithm was validated on a campus sidewalk network for wheelchair navigation. The results show an improvement in tracking a wheelchair in dense urban conditions both in accuracy and in computational time.

Income disparities among hungarian micro-regions: The mover-stayer model

2008 ◽  
Vol 58 (2) ◽  
pp. 127-156 ◽  
Author(s):  
K. Major
Keyword(s):  
Markov Chain ◽  
Markov Model ◽  
Markov Chain Model ◽  
Local Level ◽  
Relative Income ◽  
Chain Model ◽  
Ratio Test ◽  
Remarkable Feature ◽  
Homogeneity Assumption ◽  
Income Data

It is known that the simple Markov chain model overestimates the long run horizon mobility of the income distribution process. Dissolving the homogeneity assumption of the Markov model may lead to better forecasts. One generalisation of the Markov model, the Mover-Stayer model assumes heterogenous population: some units are moving according to a common Markov chain, but there are some (unknown) units that are not moving at all. They are called stayers.Based on the Frydman (1984) methodology if we compute both the Markov and Mover-Stayer models for Hungarian micro-regions income data, we find that the Mover-Stayer model fits better the regional relative income data than the simple Markov model. Using likelihood ratio test statistics we show that the difference is highly significant. The method is also applied for spatially conditioned data. The results show that the high persistence of relative income positions is a remarkable feature of the Hungarian economy in 1990–2003 both on a country-wide scale and local level. We also demonstrate that forecasts made on a less reliant model might lead to very ambiguous results.

The forecast of cultivate land quantity based on Grey-Markov model

2015 ◽  
Vol 5 (1) ◽  
pp. 127-136 ◽  
Author(s):  
Hongyan Huan ◽  
Qing-mei Tan
Keyword(s):  
Markov Chain ◽  
Markov Model ◽  
Land Use Planning ◽  
Markov Chain Model ◽  
Arable Land ◽  
Jiangsu Province ◽  
Chain Model ◽  
Cultivated Land ◽  
Content Type ◽  
Investigation Methods

Purpose – The purpose of this paper is to employ the Grey-Markov Chain Model for the scale prediction of cultivated land and took an empirical research with the case of Jiangsu province. Design/methodology/approach – Along with China’s industrialization and urbanization accelerated, a large number of cultivated land converse into construction land. The change of utilization of cultivated land concerns national food security and sustainable development of economy and society. Due to the fact that the different investigation methods of arable land usually cause a uncertain. The Grey-Markov model combines the Grey GM(1,1) and Markov chain, with two advantages of dealing with poor information and long-term and volatile series. A numeric example of scale prediction of cultivated land in Jiangsu province is also computed in the third part of the paper. Findings – The results show that the Grey-Markov Chain Model has a higher prediction accuracy compared with GM (1,1), which is a reliable guarantee for the change of cultivated land resources. Practical implications – The forecast of cultivated land can provide useful information for the general land use planning. Originality/value – The paper confirmed the feasibility of the Grey-Markov model in scale prediction of cultivated land.

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