Update tutorials/04-hidden-markov-model/index.qmd

Co-authored-by: Penelope Yong <[email protected]>

Author: JasonPekos

tutorials/04-hidden-markov-model/index.qmd

@@ -201,7 +201,9 @@ The p-values on the test suggest that we cannot reject the hypothesis that the o
 
 ## Efficient Inference With The Forward Algorithm
 
-While the above method works well for the simple example in this tutorial, some users may desire a more efficient method, especially when their model is more complicated. One simple way to improve inference is to marginalize out the hidden states of the model with an appropriate algorithm, calculating only the posterior over the continuous random variables. Not only does this allow more efficient inference via Rao-Blackwellization, but now we can sample our model with `NUTS()` alone, which is usually a much more performant MCMC kernel.
+While the above method works well for the simple example in this tutorial, some users may desire a more efficient method, especially when their model is more complicated.
+One simple way to improve inference is to marginalize out the hidden states of the model with an appropriate algorithm, calculating only the posterior over the continuous random variables.
+Not only does this allow more efficient inference via Rao-Blackwellization, but now we can sample our model with `NUTS()` alone, which is usually a much more performant MCMC kernel.
 
 Thankfully, [HiddenMarkovModels.jl](https://github.com/gdalle/HiddenMarkovModels.jl) provides an extremely efficient implementation of many algorithms related to hidden Markov models. This allows us to rewrite our model as: