Cited by 415Probability and Statistical ResearchMarkov Chains and Monte Carlo MethodsStochastic processes and statistical mechanics

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Markov Chains: From Theory to Implementation and Experimentation

Gagniuc, Paul A·2017·Wiley eBooks

DOI 10.1002/9781119387596

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“Proposition 1 [19] matrix P is an irreducible Markov matrix.”
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“Markov model, borrowed from probability theory, is a stochastics model used to model randomly changing systems [14].”
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BackgroundResult
Poéticas composicionais em peças estreadas na 21a temporada do Grupo Prelúdio 21
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Background
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Self-citation
References
Gagniuc, P. A. (2021) · Wiley eBooks · doi:10.1002/9781119698005.ref
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Influential
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Authors: Gagniuc, P. A.
Corresponding author: Gagniuc, Paul A
ORCID: Gagniuc, Paul A
Venue: Wiley eBooks
Publisher: Wiley
Published: 2017-06-22
DOI: 10.1002/9781119387596
OpenAlex: W4235646468
Cited by: 415
References: 0
Citations by year: 2026: 19 · 2025: 37 · 2024: 64 · 2023: 60 · 2022: 54 · 2021: 68 · 2020: 58 · 2019: 38 · 2018: 17
FWCI: 7.99
Type: book
Language: en
Open access: closed
Licence: doi.wiley.com/10.1002/tdm_license_1.1 (tdm)
Locations: Wiley eBooks (publishedVersion)
Topics: Physical Sciences › Mathematics › Statistics and Probability › Probability and Statistical Research
Physical Sciences › Mathematics › Statistics and Probability › Markov Chains and Monte Carlo Methods
Physical Sciences › Mathematics › Mathematical Physics › Stochastic processes and statistical mechanics
Keywords: Markov chains, stochastic processes, transition probabilities, Markov property

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Markov Chains: From Theory to Implementation and Experimentation

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