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@@ -114,6 +114,21 @@ The output should look like plot below. This conveys same information as the ind
 
 <img width="350" alt="Posterior example" src="https://github.com/user-attachments/assets/27e3e4c8-629e-4c05-ac64-f6a7bec85331">
 
+<details>
+<summary><strong>(Detailed) Additional Plots </strong></summary>
+`GetPostfitParamPlots` can produce two additional sets of plots:
+
+**Ridge Plot** - This allow to nicely see non-Gaussian paramters.
+
+<img width="350" alt="Ridge" src="https://github.com/user-attachments/assets/617f5929-b389-495e-ab7b-2ecd6c2d991e">
+
+**Violin Plot** - This also allow to see nicely non-Gaussian parameters but also is usefull in comparing two chains.
+`ProcessMCMC` must be run with option "PlotCorr" to be able to produce violin plot.
+
+<img width="350" alt="Violin example" src="https://github.com/user-attachments/assets/4788ab29-f24a-4b09-8b0f-c9b36d069cfe">
+
+</details>
+
 ### Plotting Correlation Matrix
 If you have run `ProcessMCMC` with option "PlotCorr" you will have a correlation matrix in the outputs. This is a handy tool for viewing how correlated different parameters are.
 However, mature analyses with hundreds of parameters may run into the problem of having too large of plots to be useful. To combat this, you can plot a subset of parameters using `MatrixPlotter`: