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Model Markov Chain

技能 已验证 活跃

Build and analyze discrete or continuous Markov chains including transition matrix construction, state classification, stationary distribution computation, and mean first passage times. Use when modeling a memoryless system with observed transition counts or rates, computing long-run steady-state probabilities, determining expected hitting times or absorption probabilities, classifying states as transient or recurrent, or building a foundation for hidden Markov models or reinforcement learning MDPs.

目的

To provide a robust and detailed analysis of Markov chain models, enabling users to understand and predict the long-term behavior of memoryless systems.

功能

  • Transition matrix construction
  • State classification (transient, recurrent, absorbing)
  • Stationary distribution computation
  • Mean first passage time calculation
  • Simulation-based validation

使用场景

  • Modeling memoryless systems with observed transition counts
  • Computing long-run steady-state probabilities
  • Determining expected hitting times or absorption probabilities
  • Classifying states as transient or recurrent
  • Foundation for hidden Markov models or reinforcement learning MDPs

非目标

  • Modeling systems with memory
  • Handling non-Markovian processes
  • Basic probability calculations outside of Markov chains

工作流

  1. Define state space and transitions
  2. Construct transition matrix or generator
  3. Classify states
  4. Compute stationary distribution
  5. Calculate mean first passage times
  6. Validate with simulation

实践

  • Stochastic process modeling
  • Mathematical analysis
  • Data provenance

安装

/plugin install agent-almanac@pjt222-agent-almanac

质量评分

已验证
97 /100
about 22 hours ago 分析

信任信号

最近提交2 days ago
星标14
许可证MIT
状态
查看源代码

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