Merge pull request #17 from jbytecode/main

minor fixes

paper/paper.md

@@ -51,9 +51,9 @@ As shown in @homersbi, SBI can successfully obtain the correct posterior widths
 
 # Statement of need
 
-Simulation Based Inference (SBI) covers a broad class of statistical techniques such as Approximate Bayesian Computation (ABC) [@ABC], Neural Ratio Estimation (NRE), [@NRE], Neural Likelihood Estimation (NLE) and Neural Posterior Estimation (NPE). These techniques can derive posterior distributions, conditioned of noisy data vectors, in a rigorous and efficient manner with assumptions on the data likelihood. In particular, density-estimation methods have emerged as a promising method, given their efficiency, in which generative models are used to fit likelihoods or posteriors directly using simulations.
+Simulation Based Inference (SBI) covers a broad class of statistical techniques such as Approximate Bayesian Computation (ABC) [@ABC], Neural Ratio Estimation (NRE) [@NRE], Neural Likelihood Estimation (NLE), and Neural Posterior Estimation (NPE). These techniques can derive posterior distributions, conditioned of noisy data vectors, in a rigorous and efficient manner with assumptions on the data likelihood. In particular, density-estimation methods have emerged as a promising method, given their efficiency, in which generative models are used to fit likelihoods or posteriors directly using simulations.
 
-In the field of cosmology, SBI is of particular interest due to complexity and non-linearity of models for the expectations of non-standard summary statistics of the large-scale structure, as well as the non-Gaussian noise distributions for these statistics. The assumptions required for the complex analytic modelling of these statistics - as well as the increasing dimensionality of data returned by spectroscopic and photometric galaxy surveys - limit the amount of information that can be obtained on fundamental physical parameters. Therefore, the study and research into current and future statistical methods for Bayesian inference is of paramount importance for cosmology, especially in light of current and next-generation survey missions such as DES [@Euclid], DESI [@DESI], and Euclid [@Euclid].
+In the field of cosmology, SBI is of particular interest due to complexity and non-linearity of models for the expectations of non-standard summary statistics of the large-scale structure, as well as the non-Gaussian noise distributions for these statistics. The assumptions required for the complex analytic modelling of these statistics - as well as the increasing dimensionality of data returned by spectroscopic and photometric galaxy surveys, limit the amount of information that can be obtained on fundamental physical parameters. Therefore, the study and research into current and future statistical methods for Bayesian inference is of paramount importance for cosmology, especially in light of current and next-generation survey missions such as DES [@Euclid], DESI [@DESI], and Euclid [@Euclid].
 
 The software we present, `sbiax`, is designed to be used by machine learning and physics researchers for running Bayesian inferences using density-estimation SBI techniques. These models can be fit easily with multi-accelerator training and inference within the code. This software - written in `jax` [@jax] - allows for seemless integration of cutting edge generative models to SBI, including continuous normalising flows [@ffjord], matched flows [@flowmatching], masked autoregressive flows [@mafs; @flowjax], and Gaussian mixture models - all of which are implemented in the code. The code features integration with the `optuna` [@optuna] hyper-parameter optimisation framework which would be used to ensure consistent analyses, `blackjax` [@blackjax] for fast MCMC sampling, and `equinox` [@equinox] for neural network methods. The design of `sbiax` allows for new density estimation algorithms to be trained and sampled from, as long as they conform to a simple and typical design pattern demonstrated in `sbiax`.