typo

docs/src/parameter_distributions.md

@@ -50,7 +50,7 @@ nothing # hide
 The use case `constrained_gaussian()` addresses is when prior information is qualitative, and exact distributions of the priors are unknown: i.e., the user is only able to specify the physical and likely ranges of prior parameter values at a rough, qualitative level. `constrained_gaussian()` does this by constructing a `ParameterDistribution` corresponding to a Gaussian "squashed" to fit in the given constraint interval, such that the "squashed" distribution has the specified mean and standard deviation (e.g. `prior_2` above is a log-normal for each dimension).
 
 The parameters of the Gaussian are chosen automatically (depending on the constraint) to reproduce the desired μ and σ — per the use case, other details of the form of the prior distribution shouldn't be important for downstream inference!                
-    !!! note "Slow/Failed construction?"
+!!! note "Slow/Failed construction?"
     The most common case of slow or failed construction is when requested parameters place too much mass at the hard boundary. A typical case is when the requested variance satisfies ``|\sigma| \approx \mathrm{dist}(\mu,\mathrm{boundary})`` Such priors can be defined, but not with our convenience constructor. If this is not the case but you still get failures please let us know!