Paper edits for JOSS

paper.md

@@ -34,13 +34,13 @@ Gaussian processes (GPs) [@rasmussen2006gaussian] are Bayesian nonparametric mod
 
 `GPJax` has been carefully tailored to amalgamate with the Jax ecosystem. For efficient Markov Chain Monte Carlo inference, `GPJax` can utilise samplers from BlackJax [@blackjax2021] and TensorFlow Probability [@abadi2016tensorflow]. For gradient-based optimisation, `GPJax` integrates seamlessly with Optax [@deepmind2020jax], providing a vast suite of optimisers and learning rate schedules. To efficiently represent probability distributions, `GPJax` leverages Distrax [@deepmind2020jax] and TensorFlow Probability [@abadi2016tensorflow]. To combine GPs with deep learning methods, `GPJax` can incorporate the functionality provided within Haiku [@deepmind2020jax]. The `GPJax` documentation includes examples of each of these integrations.
 
-The foundation of each abstraction given in `GPJax` is a Chex [deepmind2020jax] dataclass object. These require significantly less boilerplate code than regular Python classes, leading to a more readable codebase. Moreover, Chex dataclasses are registered as PyTree nodes, facilitating the applications of Jax operations such as just-in-time compilation and automatic differentiation to any `GPJax` object.
+The foundation of each abstraction given in `GPJax` is a Chex [@deepmind2020jax] dataclass object. These require significantly less boilerplate code than regular Python classes, leading to a more readable codebase. Moreover, Chex dataclasses are registered as PyTree nodes, facilitating the applications of Jax operations such as just-in-time compilation and automatic differentiation to any `GPJax` object.
 
 The intimacy between `GPJax` and the underlying maths also makes `GPJax` an excellent package for people new to GP modelling. Having the ability to easily cross-reference the contents of a textbook with the code that one is writing is invaluable when trying to build an intuition for a new statistical method. We further support this effort in `GPJax` through documentation that provides detailed explanations of the operations conducted within each notebook.
 
 # Wider Software Ecosystem
 
-From both an applied and methodological perspective, GPs are widely employed in the statistics and machine learning communities. High-quality software packages that promote GP modelling are accountable for much of their success. Within the Python community, the three most popular packages for GP modelling are GPFlow [@matthews2017gpflow], GPyTorch [@gardner2018gpytorch], and GPy [@gpy2014]. Despite each of these packages being indispensable tools for the community, none of them supports integration with a Jax-based workflow. `GPJax` seeks to resolve this issue. Furthermore, modern research from the GP literature, graph kernels [@borovitskiy2021matern] and Wasserstein barycentres for GPs [@mallasto2017learning], for example, are supported within `GPJax` but absent from all other packages.
+From both an applied and methodological perspective, GPs are widely employed in the statistics and machine learning communities. High-quality software packages that promote GP modelling are accountable for much of their success. Within the Python community, the three most popular packages for GP modelling are GPFlow [@matthews2017gpflow], GPyTorch [@gardner2018gpytorch], and GPy [@gpy2014]. Despite each of these packages being indispensable tools for the community, none of them support integration with a Jax-based workflow. `GPJax` seeks to resolve this issue. Furthermore, modern research from the GP literature, graph kernels [@borovitskiy2021matern] and Wasserstein barycentres for GPs [@mallasto2017learning], for example, are supported within `GPJax` but absent from these other packages.
 
 For completeness, packages written for languages other than Python include GPML [@rasmussen2010gaussian] in Matlab, GaussianProcesses.jl [@fairbrother2022gaussianprocesses], AugmentedGaussianProcesses.jl [@fajou20a] and Stheno [@stheno2022tebbutt] all in Julia.