(calibration_markov_chain_mcmc_properties_methods)=
# Chosing the MCMC algorithm

Several MCMC algorithms are available, and the desired algorithm can be selected using the `setAlgo` method, which has the following prototype:

````{only} cpp
```cpp
void setAlgo(const char* algoMCMC);
```
````

````{only} py
```python
setAlgo(algoMCMC)
```
````

The method takes a single string argument that specifies the algorithm to use. Currently, the following options are supported:

- Component-wise Metropolis–Hastings, specified with **"MH_1D"**. This method updates one parameter at a time, which is useful when parameters have 
different influences on the model. By generating new candidates along one direction at a time, it prevents less influential parameters from 
being overshadowed by the more influential ones. This approach is generally slower than the classic Metropolis–Hastings;

- Classic Metropolis–Hastings, specified with **"MH_multiD"** (default algorithm). This method generates candidates in all directions 
simultaneously, making it faster than the component-wise approach, but it may be less efficient when parameter influences vary widely.

`````{warning}
As explained, this method should be done immediately after creating the `TMCMC` object and assigning its likelihood.
`````