bump with Mhauru's change suggestions

Author: AoifeHughes

README.md

@@ -100,6 +100,6 @@ And also, kill any stray Quarto processes that are still running (sometimes it k
 pkill -9 -f quarto
 ```
 
-## License
+## Licence
 
-This project is licensed under the MIT License - see the [License](License) file for details.
+This project is licensed under the MIT Licence - see the [LICENSE](LICENSE) file for details.

_quarto.yml

@@ -208,7 +208,7 @@ format:
 
               </div>
               <div class="footer-bottom">
-                <p>Turing.jl was created by <a href="https://mlg.eng.cam.ac.uk/hong/" target="_blank" rel="noopener">Hong Ge</a>, and maintained by the core <a href="https://turinglang.org/team/" target="_blank" rel="noopener">team</a> of developers and <a href="https://github.com/TuringLang/Turing.jl/graphs/contributors" target="_blank" rel="noopener">contributors</a>.<br>© 2025 The Turing Project Contributors. <a href="https://github.com/TuringLang/Turing.jl/blob/master/LICENSE" target="_blank" rel="noopener">MIT License</a>.</p>
+                <p>Turing.jl was created by <a href="https://mlg.eng.cam.ac.uk/hong/" target="_blank" rel="noopener">Hong Ge</a>, and maintained by the core <a href="https://turinglang.org/team/" target="_blank" rel="noopener">team</a> of developers and <a href="https://github.com/TuringLang/Turing.jl/graphs/contributors" target="_blank" rel="noopener">contributors</a>.<br>© 2025 The Turing Project Contributors. <a href="https://github.com/TuringLang/Turing.jl/blob/master/LICENCE" target="_blank" rel="noopener">MIT Licence</a>.</p>
                 <a href="https://github.com/TuringLang/docs/" target="_blank" rel="noopener" class="footer-source-link"><i class="bi bi-github"></i> Website Source</a>
               </div>
             </div>

tutorials/bayesian-linear-regression/index.qmd

@@ -54,7 +54,7 @@ setprogress!(false)
 We will use the `mtcars` dataset from the [RDatasets](https://github.com/JuliaStats/RDatasets.jl) package.
 `mtcars` contains a variety of statistics on different car models, including their miles per gallon, number of cylinders, and horsepower, among others.
 
-We want to know if we can construct a Bayesian linear regression model to predict the miles per gallon of a car, given the other statistics it possesses.
+We want to know if we can construct a Bayesian linear regression model to predict the miles per gallon of a car, given the other statistics it has.
 Let us take a look at the data we have.
 
 ```{julia}

tutorials/bayesian-poisson-regression/index.qmd

@@ -222,7 +222,7 @@ The exponentiated mean of the coefficient `b1` is roughly half of that of `b2`.
 As can be seen from the plots above, the parameters converge to their final distributions after a few iterations.
 The initial samples during the [warmup phase](https://en.wikipedia.org/wiki/Markov_chain_Monte_Carlo) show higher variability and should be discarded before computing posterior statistics.
 Thus, we remove these warmup samples and view the diagnostics again.
-We discard the first 200 samples, corresponding to the adaptation phase used by the NUTS sampler (specified via the `discard_adapt=false` argument above).
+We discard the first 200 samples, corresponding to the adaptation phase used by the NUTS sampler (which we explicitly chose not to discard earlier with the `discard_adapt=false` argument).
 
 ```{julia}
 chains_new = chain[201:end, :, :]

tutorials/gaussian-processes-introduction/index.qmd

@@ -14,7 +14,7 @@ Pkg.instantiate();
 
 [JuliaGPs](https://github.com/JuliaGaussianProcesses/#welcome-to-juliagps) packages integrate well with Turing.jl because they implement the Distributions.jl
 interface.
-This tutorial assumes familiarity with [Gaussian processes](https://en.wikipedia.org/wiki/Gaussian_process); for an introduction, see [Rasmussen and Williams (2006)](http://www.gaussianprocess.org/gpml/).
+This tutorial assumes basic knowledge of [Gaussian processes](https://en.wikipedia.org/wiki/Gaussian_process) (i.e., a general understanding of what they are); for a comprehensive introduction, see [Rasmussen and Williams (2006)](http://www.gaussianprocess.org/gpml/).
 For a more in-depth understanding of the
 [JuliaGPs](https://github.com/JuliaGaussianProcesses/#welcome-to-juliagps) functionality
 used here, please consult the