diff --git a/core-functionality/index.qmd b/core-functionality/index.qmd
index c2a51e896..400418607 100755
--- a/core-functionality/index.qmd
+++ b/core-functionality/index.qmd
@@ -485,9 +485,9 @@ chn = sample(model, Prior(), 10)
 loglikelihood(model, chn)
 ```
 
-### Maximum likelihood and maximum a posterior estimates
+### Maximum likelihood and maximum a posteriori estimates
 
-Turing also has functions for estimating the maximum aposteriori and maximum likelihood parameters of a model. This can be done with
+Turing also has functions for estimating the maximum a posteriori and maximum likelihood parameters of a model. This can be done with
 
 ```{julia}
 mle_estimate = maximum_likelihood(model)
@@ -524,7 +524,7 @@ The `Gibbs` sampler can be used to specify unique automatic differentiation back
 
 For more details of compositional sampling in Turing.jl, please check the corresponding [paper](https://proceedings.mlr.press/v84/ge18b.html).
 
-### Working with filldist and arraydist
+### Working with `filldist` and `arraydist`
 
 Turing provides `filldist(dist::Distribution, n::Int)` and `arraydist(dists::AbstractVector{<:Distribution})` as a simplified interface to construct product distributions, e.g., to model a set of variables that share the same structure but vary by group.