Model Markov Chain

Construct, classify, and analyze discrete-time or continuous-time Markov chains from raw transition data or domain specifications, producing stationary distributions, mean first passage times, and simulation-based validation. Covers both DTMC and CTMC workflows end-to-end.

When to Use

• You need to model a system whose future state depends only on its current state (memoryless property)
• You have observed transition counts or rates between a finite set of states
• You want to compute long-run steady-state probabilities for a process
• You need to determine expected hitting times or absorption probabilities
• You are classifying states as transient, recurrent, or absorbing for structural analysis
• You want to compare alternative Markov models for the same system
• You are building a foundation for more advanced models (hidden Markov models, reinforcement learning MDPs)

Inputs

Required

| Input | Type | Description |