edits suggested by Anna Riha

Author: avehtari

src/reference-manual/statements.qmd

@@ -383,7 +383,7 @@ sigma ~ normal(0, 1);
 ```
 The symbol $\sim$ is called tilde. Due to historical reasons, the
 distribution statements used to be called "sampling statements" in
-Stan, but that term is not recommended anymore as it is less accurate
+Stan, but that term is not recommended anymore as it is a less accurate
 description.
 
 In general, we can read $\sim$ as "is distributed as," and overall
@@ -420,7 +420,7 @@ density.  You can think of each line as representing an additional
 piece of information.
 
 When the joint distribution is considered as a function of parameters
-(e.g. $\mu$, $\sigma$) given fixed data, it is proportional to
+(e.g. $\mu$, $\sigma$) given fixed data, it is proportional to the
 posterior distribution. In general, the posterior distribution is not
 a normalized probability density function---that is, it will be
 positive but will not in general integrate to 1---but the
@@ -432,11 +432,11 @@ terms in the model block.  Equivalently, each $\sim$ statement
 corresponds to a multiplicative factor in the unnormalized posterior
 density.
 
-Distribution statement `... ~ ...` accepts only distributions on the
-right side. These distributions can be built in or user defined
-distributions.  The left side of a distribution statement may be data,
+Distribution statements (`~`) accept only built-in or user-defined 
+distributions on the
+right side. The left side of a distribution statement may be data,
 parameter, or a complex expression, but the evaluated type needs to
-match one of the allowed type of the right hand side distribution (see
+match one of the allowed types of the distribution on the right (see
 more below).
 
 In Stan, a distribution statement is merely a notational convenience
@@ -452,13 +452,13 @@ target += normal_lpdf(sigma | 0, 1);
 ```
 
 Stan models can mix distribution statements and log probability
-increment statements. Although in the literature statistical models
-are usually defined with distributions, there are several cases due to
-computational efficiency (e.g. censored data model) or coding language
-limitations (e.g. mixture models in Stan), that we may want to code
-the log likelihood or parts of it directly, which is possible with log
-probability increment statements. See the discussion below about
-Jacobians.
+increment statements. Although statistical models 
+are usually defined with distributions in the literature, 
+there are several scenarios in which we may want to code the log
+likelihood or parts of it directly, for example, due to computational
+efficiency (e.g. censored data model) or coding language limitations
+(e.g. mixture models in Stan). This is possible with log probability
+increment statements. See also the discussion below about Jacobians.
 
 In general, a distribution statement of the form