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Mfumo wa Kielelezo cha Markov unaoendeshwa na Ajenti — Mfumo wa Kuiga wa Mseto wenye Ajenti Huru na Mabadiliko ya Hali ya Markov

Mfumo wa Kielelezo cha Markov unaoendeshwa na Ajenti (ABMM) ni mfumo wa kuiga wa mseto unaoweka mantiki ya mpito wa hali ya mnyororo wa Markov ndani ya ajenti huru binafsi. Kila ajenti huchagua kwa uhuru hali yake inayofuata kutoka kwa tumbo la mpito wa uwezekano, ikiwezesha mfumo kunasa mseto wa kiwango cha chini kati ya ajenti na muundo wa uwezekano unaoweza kudhibitiwa wa minyororo ya Markov. Njia hii hutumiwa sana katika uchumi wa afya, epidemiology, sayansi ya jamii, na utafiti wa uendeshaji.

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Vyanzo

  1. Bonabeau, E. (2002). Agent-based modeling: Methods and techniques for simulating human systems. Proceedings of the National Academy of Sciences, 99(Suppl 3), 7280-7287. DOI: 10.1073/pnas.082080899
  2. Norris, J. R. (1997). Markov Chains. Cambridge University Press, Cambridge, UK. ISBN: 9780521633963

Jinsi ya kunukuu ukurasa huu

ScholarGate. (2026, June 3). Agent-Based Markov Model — Hybrid simulation combining autonomous agents with Markov chain state transitions. ScholarGate. https://scholargate.app/sw/simulation/agent-based-markov-model

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ScholarGateAgent-based Markov model (Agent-Based Markov Model — Hybrid simulation combining autonomous agents with Markov chain state transitions). Imepatikana 2026-06-15 kutoka https://scholargate.app/sw/simulation/agent-based-markov-model · Seti ya data: https://doi.org/10.5281/zenodo.20539026