https://github.com/gagniuc/visual-basic-modules-markov-chains

These Markov Chains .BAS modules accompany the book entitled: Markov Chains: From Theory to Implementation and Experimentation, and they are compatible with Visual Basic for Applications (VBA) and Visual Basic 6.0 (VB 6.0).
https://github.com/gagniuc/visual-basic-modules-markov-chains

algorithm algorithms book experimental implementation markov-chain theory-of-probability vb6 vb6-application vb6-source

Last synced: 4 months ago
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These Markov Chains .BAS modules accompany the book entitled: Markov Chains: From Theory to Implementation and Experimentation, and they are compatible with Visual Basic for Applications (VBA) and Visual Basic 6.0 (VB 6.0).

• Host: GitHub
• URL: https://github.com/gagniuc/visual-basic-modules-markov-chains
• Owner: Gagniuc
• License: mit
• Created: 2021-10-28T21:54:24.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2022-03-16T19:00:45.000Z (over 3 years ago)
• Last Synced: 2025-01-15T07:31:51.696Z (5 months ago)
• Topics: algorithm, algorithms, book, experimental, implementation, markov-chain, theory-of-probability, vb6, vb6-application, vb6-source
• Language: VBA
• Homepage: https://www.wiley.com/en-us/Markov+Chains%3A+From+Theory+to+Implementation+and+Experimentation-p-9781119387558
• Size: 518 KB
• Stars: 7
• Watchers: 3
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Funding: .github/FUNDING.yml
  • License: LICENSE.md
  • Support: Supporting algorithm 1 (VB).bas

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README

        
# Visual Basic (VBA; VB 6.0) - Markov-Chains

These Markov Chains .BAS modules accompany the book entitled: Markov Chains: From Theory to Implementation and Experimentation, and they are compatible with Visual Basic for Applications (VBA) and Visual Basic 6.0 (VB 6.0). These algorithms include the following: 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.

![Screenshot](https://github.com/Gagniuc/Visual-Basic-modules-Markov-Chains/blob/main/mc.png?raw=true)

# References

- Paul A. Gagniuc. Markov chains: from theory to implementation and experimentation. Hoboken, NJ,  John Wiley & Sons, USA, 2017, ISBN: 978-1-119-38755-8.