Update BT docstring (#119)

Artifacts from previous docs format

crowdkit/aggregation/pairwise/bradley_terry.py

@@ -20,18 +20,15 @@ class BradleyTerry(BasePairwiseAggregator):
     according to the Bradley-Terry's probabilistic model,
     $P(i > j) = \frac{p_i}{p_i + p_j}$.
 
-    Here $\boldsymbol{p}$ is a vector of positive real-valued parameters that the algorithm optimizes. These
+    Here $\mathbf{p}$ is a vector of positive real-valued parameters that the algorithm optimizes. These
     optimization process maximizes the log-likelihood of observed comparisons outcomes by the MM-algorithm:
 
-    $L(\boldsymbol{p}) = \sum_{i=1}^n\sum_{j=1}^n[w_{ij}\ln p_i - w_{ij}\ln (p_i + p_j)]$,
+    $L(\mathbf{p}) = \sum_{i=1}^n\sum_{j=1}^n[w_{ij}\ln p_i - w_{ij}\ln (p_i + p_j)]$,
 
     where $w_{ij}$ denotes the number of comparisons of $i$ and $j$ "won" by $i$.
 
-    {% note info %}
-
-    The Bradley-Terry model needs the comparisons graph to be **strongly connected**.
-
-    {% endnote %}
+    Note:
+        The Bradley-Terry model needs the comparisons graph to be **strongly connected**.
 
     David R. Hunter.
     MM algorithms for generalized Bradley-Terry models