Update README.md

Author: Gagniuc

README.md

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 # Algorithms in Visual Basic for Applications
-Algorithms in Visual Basic for Applications (Markov Chains in VBA)
 
-A fascinating and instructive guide to Markov chains for experienced users and newcomers alike
+This repository includes the ".bas" implementations for Markov Chains that accompany the book entitled: <i>Markov Chains: From Theory to Implementation and Experimentation</i>. These ".bas" implementations can be used in various VBA Excel applications. This repository also includes an EXCEL file that supports VBA. This file is called "" and incorporates all VB ".bas" files with experimental approach and textual explanations in regard to these algorithms.
 
-This unique guide to Markov chains approaches the subject along the four convergent lines of mathematics, implementation, simulation, and experimentation. It introduces readers to the art of stochastic modeling, shows how to design computer implementations, and provides extensive worked examples with case studies.
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-Markov Chains: From Theory to Implementation and Experimentation begins with a general introduction to the history of probability theory in which the author uses quantifiable examples to illustrate how probability theory arrived at the concept of discrete-time and the Markov model from experiments involving independent variables. An introduction to simple stochastic matrices and transition probabilities is followed by a simulation of a two-state Markov chain. The notion of steady state is explored in connection with the long-run distribution behavior of the Markov chain. Predictions based on Markov chains with more than two states are examined, followed by a discussion of the notion of absorbing Markov chains. Also covered in detail are topics relating to the average time spent in a state, various chain configurations, and n-state Markov chain simulations used for verifying experiments involving various diagram configurations.
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-• Fascinating historical notes shed light on the key ideas that led to the development of the Markov model and its variants
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-• Various configurations of Markov Chains and their limitations are explored at length
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-• Numerous examples—from basic to complex—are presented in a comparative manner using a variety of color graphics
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-• All algorithms presented can be analyzed in either Visual Basic, Java Script, or PHP
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-• Designed to be useful to professional statisticians as well as readers without extensive knowledge of probability theory
 
 Covering both the theory underlying the Markov model and an array of Markov chain implementations, within a common conceptual framework, Markov Chains: From Theory to Implementation and Experimentation is a stimulating introduction to and a valuable reference for those wishing to deepen their understanding of this extremely valuable statistical tool.