Transition probabilities encode the likelihood of staying in or switching between states. In the two-state volatility example, high self-loop probabilities (0.8 for Low, 0.7 for High) mean the system tends to remain in its current state.
Conditions: The market is modeled as a two-state Markov chain.; Self-transition probabilities are high relative to switching probabilities.
Transition probabilities encode the likelihood of staying in or switching between states. In the two-state volatility example, high self-loop probabilities (0.8 for Low, 0.7 for High) mean the system tends to remain in its current state.
Conditions: The market is modeled as a two-state Markov chain.; Self-transition probabilities are high relative to switching probabilities.
A trace plot visualizes the sequence of sample values generated by an MCMC chain over iterations. To determine stationarity, look for the point where the values stop converging and begin to fluctuate around a stable equilibrium, indicating the end of the burn-in period.
Conditions: Used for diagnosing MCMC convergence.; Requires plotting sample values against iteration numbers.; The plot shows both the burn-in and stationary phases.
A trace plot visualizes the sequence of sample values generated by an MCMC chain over iterations. To determine stationarity, look for the point where the values stop converging and begin to fluctuate around a stable equilibrium, indicating the end of the burn-in period.
Conditions: Used for diagnosing MCMC convergence.; Requires plotting sample values against iteration numbers.; The plot shows both the burn-in and stationary phases.