Fix formatting

learning/structure/index.md

@@ -15,7 +15,7 @@ The score metrics for a structure $$\mathcal{G}$$ and data $$D$$ can be generall
 
 $$ Score(G:D) = LL(G:D) - \phi(|D|) \|G\|. $$
 
-Here $$LL(G:D)$$ refers to the log-likelihood of the data under the graph structure $$\mathcal{G}$$. The parameters in the Bayesian network $$G$$ are estimated based on MLE and the log-likelihood score is calculated based on the estimated parameters. If the score function only consisted of the log-likelihood term, then the optimal graph would be a complete graph, which is probably overfitting the data. Instead, the second term $$\phi(|D|) \|G\|$$ in the scoring function serves as a regularization term, favoring simpler models. $$\lvert D \rvert$$ is the number of data samples, and $$\|G\|$$ is the number of parameters in the graph $$\mathcal{G}$$. When $$\phi(t) = 1$$, the score function is known as the Akaike Information Criterion (AIC). When $$\phi(t) = \log(t)/2$$, the score function is known as the Bayesian Information Criterion (BIC). With the BIC, the influence of model complexity decreases as $$\lvert D \rvert$$ grows, allowing the log-likelihood term to eventually dominate the score.
+Here $$LL(G:D)$$ refers to the log-likelihood of the data under the graph structure $$\mathcal{G}$$. The parameters in the Bayesian network $$G$$ are estimated based on MLE and the log-likelihood score is calculated based on the estimated parameters. If the score function only consisted of the log-likelihood term, then the optimal graph would be a complete graph, which is probably overfitting the data. Instead, the second term $$\phi(\lvert D \rvert) \lVert G \rVert$$ in the scoring function serves as a regularization term, favoring simpler models. $$\lvert D \rvert$$ is the number of data samples, and $$\|G\|$$ is the number of parameters in the graph $$\mathcal{G}$$. When $$\phi(t) = 1$$, the score function is known as the Akaike Information Criterion (AIC). When $$\phi(t) = \log(t)/2$$, the score function is known as the Bayesian Information Criterion (BIC). With the BIC, the influence of model complexity decreases as $$\lvert D \rvert$$ grows, allowing the log-likelihood term to eventually dominate the score.
 
 There is another family of Bayesian score function called BD (Bayesian Dirichlet) score. For BD score, if first defines the probability of data $$D$$ conditional on the graph structure $$\mathcal{G}$$ as