Update tutorials/bayesian-differential-equations/index.qmd

Author: penelopeysm

tutorials/bayesian-differential-equations/index.qmd

@@ -113,7 +113,8 @@ To avoid this happening, we tell the solver to aim for small absolute and relati
 We also add a fudge factor `ϵ = 1e-5` to the predicted data.
 Since `ϵ` is greater than the solver's tolerance, it should overcome any remaining numerical error, making sure all predicted values are positive.
 At the same time, it is so small compared to the data that it should have a negligible effect on inference.
-If this approach doesn't work, there are some more ideas to try [here](https://docs.sciml.ai/DiffEqDocs/stable/basics/faq/#My-ODE-goes-negative-but-should-stay-positive,-what-tools-can-help?). In the case of continuous observations (e.g. deriving from modelling chemical reactions), it is sufficient to use a Normal distribution with the mean as the data point and an appropriately chosen variance (which can also be a parameter with a prior distribution).
+If this approach doesn't work, there are some more ideas to try [here](https://docs.sciml.ai/DiffEqDocs/stable/basics/faq/#My-ODE-goes-negative-but-should-stay-positive,-what-tools-can-help?).
+In the case of continuous observations (e.g. data derived from modelling chemical reactions), it is sufficient to use a normal distribution with the mean as the data point and an appropriately chosen variance (which can itself also be a parameter with a prior distribution).
 
 ```{julia}
 @model function fitlv(data, prob)