scholarly journals The Markov Process as a Model of Migration Based on the Example of the Movement of Banknotes

2021 ◽  
Vol 2/2021 (35) ◽  
pp. 76-92
Author(s):  
Arkadiusz Manikowski ◽  
Keyword(s):  
Markov Chain ◽  
Gravity Model ◽  
Transition Probabilities ◽  
Markov Chain Model ◽  
Transition Probability ◽  
Empirical Models ◽  
Chain Model ◽  
National Bank ◽  
One Step ◽  
Step Transition

This paper presents a way of using the Markov chain model for the analysis of migration based on the example of banknote migration between regions in Poland. We have presented the application of the methodology for estimating one-step transition probabilities for the Markov chain based on macro-data gathered during the project conducted in the National Bank of Poland (NBP) in the period of December 2015–2018. We have shown the usefulness of state-aggregated Markov chain not only as a model of banknote migration but as migration in general. The banknotes are considered here as goods, so their migration is strictly related to, inter alia, the movement of people (commuting to work, business trips, etc.).Thus, the gravity-like properties of cash migration pointed to the gravity model as one of the most pervasive empirical models in regional science. Transition probability of the Markov chain expressing the attractive force between regions allows for estimating the gravity model for the identification of relevant reasons of note and, consequently, people migration.

Probability of maintaining or attaining a structure in one step

1987 ◽  
Vol 24 (4) ◽  
pp. 1006-1011 ◽  
Author(s):  
G. Abdallaoui
Keyword(s):  
Markov Chain ◽  
Discrete Time ◽  
Transition Probabilities ◽  
Markov Chain Model ◽  
Chain Model ◽  
Discrete Time Markov Chain ◽  
One Step ◽  
Manpower System

Our concern is with a particular problem which arises in connection with a discrete-time Markov chain model for a graded manpower system. In this model, the members of an organisation are classified into distinct classes. As time passes, they move from one class to another, or to the outside world, in a random way governed by fixed transition probabilities. In this paper, the emphasis is placed on evaluating exact values of the probabilities of attaining and maintaining a structure.

Degradation modelling and life expectancy using Markov chain model for carriageway

2018 ◽  
Vol 35 (6) ◽  
pp. 1268-1288 ◽  
Author(s):  
Kong Fah Tee ◽  
Ejiroghene Ekpiwhre ◽  
Zhang Yi
Keyword(s):  
Markov Chain ◽  
Life Expectancy ◽  
Transition Probabilities ◽  
Markov Chain Model ◽  
Transition Probability ◽  
Condition Assessment ◽  
Cumulative Distribution ◽  
Chain Model ◽  
Content Type ◽  
Pairwise Correlation

PurposeAutomated condition surveys have been recently introduced for condition assessment of highway infrastructures worldwide. Accurate predictions of the current state, median life (ML) and future state of highway infrastructures are crucial for developing appropriate inspection and maintenance strategies for newly created as well as existing aging highway infrastructures. The paper aims to discuss these issues.Design/methodology/approachThis paper proposes Markov Chain based deterioration modelling using a linear transition probability (LTP) matrix method and a median life expectancy (MLE) algorithm. The proposed method is applied and evaluated using condition improvement between the two successive inspections from the Surface Condition Assessment of National Network of Roads survey of the UK Pavement Management System.FindingsThe proposed LTP matrix model utilises better insight than the generic or decoupling linear approach used in estimating transition probabilities formulated in the past. The simulated LTP predicted conditions are portrayed in a deterioration profile and a pairwise correlation. The MLs are computed statistically with a cumulative distribution function plot.Originality/valueThe paper concludes that MLE is ideal for projecting half asset life, and the LTP matrix approach presents a feasible approach for new maintenance regime when more certain deterioration data become available.

Probability of maintaining or attaining a structure in one step

1987 ◽  
Vol 24 (04) ◽  
pp. 1006-1011 ◽  
Author(s):  
G. Abdallaoui
Keyword(s):  
Markov Chain ◽  
Discrete Time ◽  
Transition Probabilities ◽  
Markov Chain Model ◽  
Chain Model ◽  
Discrete Time Markov Chain ◽  
One Step ◽  
Manpower System

Our concern is with a particular problem which arises in connection with a discrete-time Markov chain model for a graded manpower system. In this model, the members of an organisation are classified into distinct classes. As time passes, they move from one class to another, or to the outside world, in a random way governed by fixed transition probabilities. In this paper, the emphasis is placed on evaluating exact values of the probabilities of attaining and maintaining a structure.

Object Trajectory Information for Robotic Service in Intelligent Space

2014 ◽  
Vol 490-491 ◽  
pp. 1172-1176
Author(s):  
Weerachai Skulkittiyut ◽  
Toru Yamaguchi ◽  
Makoto Mizukawa
Keyword(s):  
Markov Chain ◽  
Transition Probabilities ◽  
Markov Chain Model ◽  
Transition Probability ◽  
Basic Knowledge ◽  
Chain Model ◽  
Object Knowledge ◽  
Individual Object ◽  
Intelligent Space

Implementation of robotic service is needed extremely enormous of information including environment, place, user and object knowledge. It is extremely difficult for a robot to do tasks ordered by a human without having some basic knowledge or information. This paper proposes a method to create object knowledge focusing on an object and objects place relationship for the robotic service namely tidy-up service, in which a robot is asked to take objects such as books, cups, dishes on a table to appropriate places automatically. In details, as the first phase, we conduct a questionnaire to collect the trajectories in term of places of individual object from the participants. Based on the collected object trajectory information, we are able to build Markov chain model of the object which the states are possible places and transition probabilities are the probability that the object moved from one place to other places. In final, we are able to use the transition probability including the Markov chain model to predict and provide the next appropriate place. The result showed that the proposed approach is efficient in creating object trajectory as knowledge, hence, helping the robots to and to provide intuitive service.

scholarly journals Markov Chain-Based Acute Effect Estimation of Air Pollution on Elder Asthma Hospitalization

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Li Luo ◽  
Fengyi Zhang ◽  
Wei Zhang ◽  
Lin Sun ◽  
Chunyang Li ◽  
...  
Keyword(s):  
Air Pollution ◽  
Markov Chain ◽  
Transition Probabilities ◽  
Markov Chain Model ◽  
Transition Probability ◽  
Significant Risk ◽  
Significant Risk Factor ◽  
Asthma Patient ◽  
Chain Model ◽  
Asthma Hospitalization

Background. Asthma caused substantial economic and health care burden and is susceptible to air pollution. Particularly, when it comes to elder asthma patient (older than 65), the phenomenon is more significant. The aim of this study is to investigate the Markov-based acute effects of air pollution on elder asthma hospitalizations, in forms of transition probabilities. Methods. A retrospective, population-based study design was used to assess temporal patterns in hospitalizations for asthma in a region of Sichuan province, China. Approximately 12 million residents were covered during this period. Relative risk analysis and Markov chain model were employed on daily hospitalization state estimation. Results. Among PM2.5, PM10, NO2, and SO2, only SO2 was significant. When air pollution is severe, the transition probability from a low-admission state (previous day) to high-admission state (next day) is 35.46%, while it is 20.08% when air pollution is mild. In particular, for female-cold subgroup, the counterparts are 30.06% and 0.01%, respectively. Conclusions. SO2 was a significant risk factor for elder asthma hospitalization. When air pollution worsened, the transition probabilities from each state to high admission states increase dramatically. This phenomenon appeared more evidently, especially in female-cold subgroup (which is in cold season for female admissions). Based on our work, admission amount forecast, asthma intervention, and corresponding healthcare allocation can be done.

Data Mining Technique Applied To DNA Sequencing

2015 ◽  
Vol 2 (7) ◽  
pp. 18
Author(s):  
R. Jamuna
Keyword(s):  
Data Mining ◽  
Dna Methylation ◽  
Markov Chain ◽  
Human Genome ◽  
Transition Probabilities ◽  
Markov Chain Model ◽  
Effective Means ◽  
Cpg Islands ◽  
Chain Model ◽  
The Given

CpG islands (CGIs) play a vital role in genome analysis as genomic markers.  Identification of the CpG pair has contributed not only to the prediction of promoters but also to the understanding of the epigenetic causes of cancer. In the human genome [1] wherever the dinucleotides CG occurs the C nucleotide (cytosine) undergoes chemical modifications. There is a relatively high probability of this modification that mutates C into a T. For biologically important reasons the mutation modification process is suppressed in short stretches of the genome, such as ‘start’ regions. In these regions [2] predominant CpG dinucleotides are found than elsewhere. Such regions are called CpG islands. DNA methylation is an effective means by which gene expression is silenced. In normal cells, DNA methylation functions to prevent the expression of imprinted and inactive X chromosome genes. In cancerous cells, DNA methylation inactivates tumor-suppressor genes, as well as DNA repair genes, can disrupt cell-cycle regulation. The most current methods for identifying CGIs suffered from various limitations and involved a lot of human interventions. This paper gives an easy searching technique with data mining of Markov Chain in genes. Markov chain model has been applied to study the probability of occurrence of C-G pair in the given   gene sequence. Maximum Likelihood estimators for the transition probabilities for each model and analgously for the  model has been developed and log odds ratio that is calculated estimates the presence or absence of CpG is lands in the given gene which brings in many  facts for the cancer detection in human genome.

scholarly journals Calibration of Transition Intensities for a Multistate Model: Application to Long-Term Care

Risks ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 37
Author(s):  
Manuel L. Esquível ◽  
Gracinda R. Guerreiro ◽  
Matilde C. Oliveira ◽  
Pedro Corte Real
Keyword(s):  
Markov Chain ◽  
Continuous Time ◽  
Long Term Care ◽  
Transition Probabilities ◽  
Markov Chain Model ◽  
Chain Model ◽  
Chain Process ◽  
Term Care ◽  
National Network

We consider a non-homogeneous continuous time Markov chain model for Long-Term Care with five states: the autonomous state, three dependent states of light, moderate and severe dependence levels and the death state. For a general approach, we allow for non null intensities for all the returns from higher dependence levels to all lesser dependencies in the multi-state model. Using data from the 2015 Portuguese National Network of Continuous Care database, as the main research contribution of this paper, we propose a method to calibrate transition intensities with the one step transition probabilities estimated from data. This allows us to use non-homogeneous continuous time Markov chains for modeling Long-Term Care. We solve numerically the Kolmogorov forward differential equations in order to obtain continuous time transition probabilities. We assess the quality of the calibration using the Portuguese life expectancies. Based on reasonable monthly costs for each dependence state we compute, by Monte Carlo simulation, trajectories of the Markov chain process and derive relevant information for model validation and premium calculation.

Single-shelf library-type Markov chains with infinitely many books

1977 ◽  
Vol 14 (02) ◽  
pp. 298-308 ◽  
Author(s):  
Peter R. Nelson
Keyword(s):  
Markov Chain ◽  
Markov Chains ◽  
Transition Probabilities ◽  
Natural Order ◽  
Order Book ◽  
Exit Boundary ◽  
One Step ◽  
Step Transition ◽  
The Right ◽  
Positive Recurrent

In a single-shelf library having infinitely many books B 1 , B 2 , …, the probability of selecting each book is assumed known. Books are removed one at a time and replaced in position k prior to the next removal. Books are moved either to the right or the left as is necessary to vacate position k. Those arrangements of books where after some finite position all the books are in natural order (book i occupies position i) are considered as states in an infinite Markov chain. When k > 1, we show that the chain can never be positive recurrent. When k = 1, we find the limits of ratios of one-step transition probabilities; and when k = 1 and the chain is transient, we find the Martin exit boundary.

scholarly journals Will this search end up with booking? Modeling airline booking conversion of anonymous visitors

2020 ◽  
Vol 27 (2) ◽  
pp. 237-250
Author(s):  
Misuk Lee
Keyword(s):  
Markov Chain ◽  
Transition Probabilities ◽  
Markov Chain Model ◽  
Distribution Channel ◽  
Stochastic Approach ◽  
Tourism Industry ◽  
Chain Model ◽  
Online Search ◽  
Clickstream Data ◽  
Content Type

Purpose Over the past two decades, online booking has become a predominant distribution channel of tourism products. As online sales have become more important, understanding booking conversion behavior remains a critical topic in the tourism industry. The purpose of this study is to model airline search and booking activities of anonymous visitors. Design/methodology/approach This study proposes a stochastic approach to explicitly model dynamics of airline customers’ search, revisit and booking activities. A Markov chain model simultaneously captures transition probabilities and the timing of search, revisit and booking decisions. The suggested model is demonstrated on clickstream data from an airline booking website. Findings Empirical results show that low prices (captured as discount rates) lead to not only booking propensities but also overall stickiness to a website, increasing search and revisit probabilities. From the decision timing of search and revisit activities, the author observes customers’ learning effect on browsing time and heterogeneous intentions of website visits. Originality/value This study presents both theoretical and managerial implications of online search and booking behavior for airline and tourism marketing. The dynamic Markov chain model provides a systematic framework to predict online search, revisit and booking conversion and the time of the online activities.

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