# Results and Comparison

## Results for the Linear Parameter Estimation

Results of linear parameter estimation can be visualized with the method 'linfitsvdPlot'

```

params = {...
'FEEPS_XX', ...
'CAPACT_TM2_XX', ...
'IFO_X12X1', ...
'EOM_TM1_STIFF_XX', ...
'EOM_TM2_STIFF_XX'};

values = [0.82 1.08 0.0004 1.3e-6 1.9e-6];

plshow = plist(...
'FitParams', params,...
'FitParamsValues', values);

linfitsvdPlot(fpars, plshow);

```

## Results for the MCMC

If our pest object obtained with MCMC is b, then we can plot the results, or print them to screen with the following piece of code:
```

pl = plist(...
'burnin', 1000, ...                   % The number of samples to be discarded from the chain
'pdfs', true, ...                     % Plot the pdfs of the parameters
'chain', 0 ,...                       % Do not plot chains
'separate pdfs', [1 2 3 4 5], ...     % Plot the pdfs in separate figures
'nbins', 20, ...                      % Number of bins for the histograms
'colormap', summer, ...               % choose colormap for cosmetics!
'results', true);                     % Print results to screen

mcmcPlot(b,pl)

```
Note: Here we assumed that we used the simplex algorithm before the mcmc sampling as in section 5.6.1. The PDFs of the parameters will look like the following:

## Comparison

The table below show the comparison of the results obtained with the two methods. We compare as well the error on the parameter with the optimal error expected from the Fisher matrix analysis

Parameter True value Exp. error Linear MCMC
FEEPS_XX 0.82 0.0002 0.8185 ± 3e-4 0.8201 ± 2.5e-04
CAPACT_TM2_XX 1.08 3.0e-06 1.080007 ± 4e-06 1.080004 ± 3e-06
IFO_X12X1 0.0004 1.1e-07 3.998e-04 ± 1.5e-07 4.000e-04 ± 1e-07
EOM_TM1_STIFF_XX 1.3e-6 1.7e-10 1.3004e-06 ± 2.0e-10 1.2999e-06 ± 1.6e-10
EOM_TM2_STIFF_XX 1.9e-6 1.6e-10 1.9002e-06 ± 2.0e-10 1.8999e-06 ± 1.5e-10