Using Discrete Markov Chains in Prediction of Health Economics Behaviour

The aim of this article is show the concept of using of the Discrete Markov Chains to predict economic phenomena. This subject is important for two reasons. The first of them are models based on Markov chains use the statistical informations obtained during the investigation processes. Another important reason is the fact that this way of modeling is highly flexible and can be used to simulation of economic phenomenas. In this paper authors describe the idea of modeling and present the example of simply model of patient population of primary health care and show preliminary simulation results.

[1]  Piotr Milczarski,et al.  Gender recogniotion methods useful in mobile authentication applications , 2016 .

[2]  Jacob Roll,et al.  On the Input-Output Representation of Piecewise Affine State Space Models , 2010, IEEE Transactions on Automatic Control.

[3]  Allen Hutchinson,et al.  The european primary care monitor: structure, process and outcome indicators , 2010, BMC family practice.

[4]  Roman Garnett,et al.  Bayesian optimization for sensor set selection , 2010, IPSN '10.

[5]  Zi-Jiang Yang,et al.  Predictive control for dual-rate systems based on lifted state-space model identified by N4SID method , 2009, Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference.

[6]  T. Górecki,et al.  Analiza konwergencji podregionów za pomocą łańcuchów Markowa , 2012 .

[8]  Kevin Leyton-Brown,et al.  Auto-WEKA: combined selection and hyperparameter optimization of classification algorithms , 2012, KDD.

[9]  Julie Beth Lovins,et al.  Development of a stemming algorithm , 1968, Mech. Transl. Comput. Linguistics.

[10]  Min Gan,et al.  A Self-Organizing State Space Type Microstructure Model for Financial Asset Allocation , 2016, IEEE Access.

[11]  Roope Raisamo,et al.  An experimental comparison of gender classification methods , 2008, Pattern Recognit. Lett..

[12]  Tadeusz Kwater,et al.  THE SIMULATIONS OF SEQUENTIAL OF ESTIMATORS FOR OBJECTS WITH A SERIAL STRUCTURE , 2016 .

[13]  Piotr Milczarski,et al.  An Approach to Brain Thinker Type Recognition Based on Facial Asymmetry , 2010, ICAISC.

[14]  Chao-Fu Hong,et al.  Semantic Methods for Knowledge Management and Communication , 2011 .

[15]  Stanislaw Galus.  Dictionary-Based Part-of-Speech Tagging of Polish , 2005, Intelligent Information Systems.

[16]  Jerzy Tchorzewski,et al.  Identification of the polish power exchange based on the data related to the day-ahead market , 2016, 2016 Electric Power Networks (EPNet).

[17]  Simo Särkkä,et al.  On Gaussian Optimal Smoothing of Non-Linear State Space Models , 2010, IEEE Transactions on Automatic Control.

[18]  Bao-Liang Lu,et al.  Multi-view Gender Classification Using Local Binary Patterns and Support Vector Machines , 2006, ISNN.

[19]  Angelia Nedic,et al.  Distributed and Recursive Parameter Estimation in Parametrized Linear State-Space Models , 2008, IEEE Transactions on Automatic Control.

[20]  Wook Hyun Kwon,et al.  LMS finite memory estimators for discrete-time state space models , 2009, Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference.

[21]  Wykorzystanie łańcuchów decyzyjnych Markowa do analizy portfelowej , 2008 .

[22]  J. Tchórzewski.  Capabilities of MATLAB and Simulink related to modelling of Polish power exchange , 2016 .

[23]  John Skilling,et al.  Data analysis : a Bayesian tutorial , 1996 .

[24]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[25]  R. Baierlein.  Probability Theory: The Logic of Science , 2004 .

[26]  Waldemar Karwowski,et al.  Automatic Indexer for Polish Agricultural Texts , 2014 .

[27]  Tunga Güngör,et al.  Part-of-Speech Tagging , 2005 .

[28]  Aleix M. Martinez,et al.  The AR face database , 1998 .

[29]  Hyeonjoon Moon,et al.  The FERET evaluation methodology for face-recognition algorithms , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[30]  Bernhard E. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.

[31]  Piotr Milczarski,et al.  Creation of the fuzzy three-level adapting brainthinker , 2013, 2013 6th International Conference on Human System Interactions (HSI).

[32]  Jerzy Tchórzewski,et al.  Identification modeling of Polish electric power exchange , 2016 .

[33]  David D. Cox,et al.  Hyperopt: A Python Library for Optimizing the Hyperparameters of Machine Learning Algorithms , 2013, SciPy.

[34]  P. Groenewegen,et al.  Measures of quality, costs and equity in primary health care instruments developed to analyse and compare primary care in 35 countries. , 2013, Quality in primary care.

[35]  Waldemar Karwowski,et al.  Methods of automatic topic mining in publications in agriculture domain , 2017 .