changed title formatting and small fixes

Author: dwijenchawra

docs/src/concepts.md

@@ -3,7 +3,8 @@
 
 ## Bayesian Networks
 
-A Bayesian Network (BN) represents a probability distribution over a set of variables, ``P(x_1, x_2, \ldots, x_n)``. Bayesian networks leverage variable relations in order to efficiently decompose the joint distribution into smaller conditional probability distributions.
+A Bayesian Network (BN) represents a probability distribution over a set of variables, ``P(x_1, x_2, \ldots, x_n)``. 
+Bayesian networks leverage variable relations in order to efficiently decompose the joint distribution into smaller conditional probability distributions.
 
 A BN is defined by a directed acyclic graph and a set of conditional probability distributions. Each node in the graph corresponds to a variable ``x_i`` and is associated with a conditional probability distribution ``P(x_i \mid \text{parents}(x_i))``.
 

docs/src/usage.md

@@ -183,7 +183,7 @@ gsampler = GibbsSampler(evidence, burn_in=500, thinning=1, initial_sample=initia
 rand(bn_gibbs, gsampler, 5)
 ```
 
-# Parameter Learning
+## Parameter Learning
 
 BayesNets.jl supports parameter learning for an entire graph.
 
@@ -345,7 +345,7 @@ table(bn, :b) # 3
 table(bn, :c, :a=>1) # 4
 ```
 
-# Reading from XDSL
+## Reading from XDSL
 
 Discrete Bayesian Networks can be read from the .XDSL file format.
 
@@ -358,9 +358,9 @@ TikzPictures.save(SVG("plot10"), plot) # hide
 
 ![](plot10.svg)
 
-# Bayesian Score for a Network Structure
+## Bayesian Score for a Network Structure
 
-The bayesian score for a discrete-valued BayesNet can can be calculated based only on the structure and data (the CPDs do not need to be defined beforehand). This is implemented with a method of `bayesian_score` that takes in a directed graph, the names of the nodes and data.
+The bayesian score for a discrete-valued BayesNet can can be calculated based only on the structure and data (the CPDs do not need to be defined beforehand). This is implemented with a method of ```bayesian_score``` that takes in a directed graph, the names of the nodes and data.
 
 ```@example bayesnet
 data = DataFrame(c=[1,1,1,1,2,2,2,2,3,3,3,3],