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“Chapter 11 Markov Chains.” Dartmouth, dartmouth.edu/~chance/teaching_aids/books_articles/probability_book/Chapter11.pdf. (Online PDF) | “Chapter 11 Markov Chains.” Dartmouth, dartmouth.edu/~chance/teaching_aids/books_articles/probability_book/Chapter11.pdf. (Online PDF) | ||
| − | “Markov Chains.” Brilliant Math & Science Wiki, brilliant.org/wiki/markov-chains/. | + | Jurafsky, Daniel, and James H Martin. “Hidden Markov Models.” Stanford, 2 Oct. 2019, web.stanford.edu/~jurafsky/slp3/A.pdf. (Online PDF) |
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| + | “Markov Chains.” Brilliant Math & Science Wiki, brilliant.org/wiki/markov-chains/. | ||
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| + | “Markov Chains.” Stanford, web.stanford.edu/class/stat217/New12.pdf. (Online PDF) | ||
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| + | Nicholson, Chris. “A Beginner's Guide to Generative Adversarial Networks (GANs).” Pathmind, wiki.pathmind.com/generative-adversarial-network-gan. | ||
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| + | Scheepers, Herman. “Markov Chain Analysis and Simulation Using Python.” Medium, Towards Data Science, 23 Nov. 2019, towardsdatascience.com/markov-chain-analysis-and-simulation-using-python-4507cee0b06e. | ||
Weissman, Alex. “Going Steady (State) with Markov Processes.” Bloomington Tutors Blog, bloomingtontutors.com/blog/going-steady-state-with-markov-processes. | Weissman, Alex. “Going Steady (State) with Markov Processes.” Bloomington Tutors Blog, bloomingtontutors.com/blog/going-steady-state-with-markov-processes. | ||
Latest revision as of 13:08, 6 December 2020
Additional Readings
https://www.math.fsu.edu/~cstover/teaching/sp18_mas3105/handouts/ch1/REFetc.pdf - An introduction to elementary row operations, row echelon form (ROF), and reduced row echelon form (RREF).
https://setosa.io/ev/markov-chains/ - An interesting visual introduction of Markov chains. Includes a manipulatable transition matrix with a dynamic diagram to show its effects in real time.
http://langvillea.people.cofc.edu/MCapps7.pdf - Introduces and explains five historic applications of Markov chains.
References
Barwe. “采样2 - 离散马尔可夫链的几个性质.” 阔落煮酒, 9 July 2019, chenyin.top/stat/20190416-ea6c.html.
Bertsekas, Dimitri P., and John N. Tsitsiklis. Introduction to Probability. Athena Scientific, 2008.
Bulla, Jan. “5 - Basic Structure of a Hidden Markov Model.” ResearchGate, Jan. 2006, www.researchgate.net/figure/Basic-structure-of-a-Hidden-Markov-Model_fig2_24115579.
“Chapter 11 Markov Chains.” Dartmouth, dartmouth.edu/~chance/teaching_aids/books_articles/probability_book/Chapter11.pdf. (Online PDF)
Jurafsky, Daniel, and James H Martin. “Hidden Markov Models.” Stanford, 2 Oct. 2019, web.stanford.edu/~jurafsky/slp3/A.pdf. (Online PDF)
“Markov Chains.” Brilliant Math & Science Wiki, brilliant.org/wiki/markov-chains/.
“Markov Chains.” Stanford, web.stanford.edu/class/stat217/New12.pdf. (Online PDF)
Nicholson, Chris. “A Beginner's Guide to Generative Adversarial Networks (GANs).” Pathmind, wiki.pathmind.com/generative-adversarial-network-gan.
Scheepers, Herman. “Markov Chain Analysis and Simulation Using Python.” Medium, Towards Data Science, 23 Nov. 2019, towardsdatascience.com/markov-chain-analysis-and-simulation-using-python-4507cee0b06e.
Weissman, Alex. “Going Steady (State) with Markov Processes.” Bloomington Tutors Blog, bloomingtontutors.com/blog/going-steady-state-with-markov-processes.
WU, JUN. BEAUTY OF MATHEMATICS. CHAPMAN & HALL CRC, 2018.
