dollars to ticks (#50)

doc/src/dfs.md

@@ -6,11 +6,13 @@
 ### Definition
 The DFS is defined as the trace of the product of the observation sensitivity matrix and the observation error covariance matrix. Mathematically, it can be expressed as:
 
-$$ \text{DFS} = \text{Tr}(\mathbf{K} \mathbf{H}) $$
+```math
+\text{DFS} = \text{Tr}(\mathbf{K} \mathbf{H})
+```
 
 where:
-- $\mathbf{K}$ is the Kalman gain matrix, which represents how much weight is given to the observations in the assimilation process.
-- $\mathbf{H}$ is the observation operator, which maps the model state variables to the observed variables.
+- ``\mathbf{K}`` is the Kalman gain matrix, which represents how much weight is given to the observations in the assimilation process.
+- ``\mathbf{H}`` is the observation operator, which maps the model state variables to the observed variables.
 
 ### Interpretation
 1. **Information Content**: DFS indicates how much the observations have influenced the analysis. A higher DFS means that the observations have a significant impact on the analysis, providing more information.