---
myst:
    substitutions:
        sentence: (see {{metho}} for more details)
---
```{include} /../core/calibration/markov_chain.md
```

The usage of the `TMCMC` class can be summarised in a few key steps:

1. Prepare the data and the model:
   - The parameters to be calibrated must be instances of classes inheriting from `TStochasticAttribute`;
   - Select the assessor type and construct the `TMCMC` object with the appropriate likelihood function (see [](#calibration_markov_chain_tmcmc)).

2. Set the algorithm properties:
   - Define optional behaviours;
   - Specify the uncertainty hypotheses via the dedicated methods (see [](#calibration_markov_chain_mcmc_properties)).

3. Perform the estimate:
   - Run the estimate process;
   - Eventually continue it if the convergence is not reached (see [](#calibration_markov_chain_run)).

4. Perform post-processing:
   - Investigate the quality of the samples through diagnostics and plots (see [](#calibration_markov_chain_diagnostics)).

5. Analyse the results:
   - Extract the results and visualise them with the standard plotting tools (see [](#calibration_markov_chain_results)).

```{toctree}
markov_chain/tmcmc
markov_chain/mcmc_properties
markov_chain/run
markov_chain/diagnostics
markov_chain/results
```

Two examples are also provided in the use-case section (see [](#use_cases_macro_calibration_mcmc) and [](#use_cases_macro_calibration_mcmc_linReg)).