minor edits to text and update or Chang ref

paper/paper.bib

@@ -52,13 +52,20 @@ @article{lee2023switching
   year={2023}
 }
 
-@article{chang2023low,
-  title={Low-rank extended {K}alman filtering for online learning of neural networks from streaming data},
-  author={Chang, Peter G and Dur{\'a}n-Mart{\'\i}n, Gerardo and Shestopaloff, Alexander Y and Jones, Matt and Murphy, Kevin},
-  journal={arXiv preprint arXiv:2305.19535},
-  year={2023}
+@inproceedings{chang2023low,
+  title = 	 {Low-rank extended {K}alman filtering for online learning of neural networks from streaming data},
+  author =       {Chang, Peter G. and Dur\'an-Mart\'in, Gerardo and Shestopaloff, Alex and Jones, Matt and Murphy, Kevin P},
+  booktitle = 	 {Proceedings of The 2nd Conference on Lifelong Learning Agents},
+  pages = 	 {1025--1071},
+  year = 	 {2023},
+  editor = 	 {Chandar, Sarath and Pascanu, Razvan and Sedghi, Hanie and Precup, Doina},
+  volume = 	 {232},
+  series = 	 {Proceedings of Machine Learning Research},
+  month = 	 {22--25 Aug},
+  publisher =    {PMLR},
 }
 
+
 @article{weinreb2024keypoint,
   author = {Weinreb, Caleb and Pearl, Jonah E. and Lin, Sherry and Osman, Mohammed Abdal Monium and Zhang, Libby and Annapragada, Sidharth and Conlin, Eli and Hoffmann, Red and Makowska, Sofia and Gillis, Winthrop F. and Jay, Maya and Ye, Shaokai and Mathis, Alexander and Mathis, Mackenzie W. and Pereira, Talmo and Linderman, Scott W. and Datta, Sandeep Robert},
   date = {2024/07/01},

paper/paper.md

@@ -56,17 +56,17 @@ More information about state space models and algorithms for state inference and
 
 # Statement of need
 
-`Dynamax` is an open-source Python pacakge for state space modeling. Since it is built with `JAX` [@jax], it supports just-in-time (JIT) compilation for hardware acceleration on CPU, GPU, and TPU machines. It also supports automatic differentiation for gradient-based model learning. While other libraries exist for state space modeling in Python (and some also use `JAX`), this library provides a combination of low-level inference algorithms and high-level modeling objects that can support a wide range of research applications.
+`Dynamax` is an open-source Python pacakge for state space modeling. Since it is built with `JAX` [@jax], it supports just-in-time (JIT) compilation for hardware acceleration on CPU, GPU, and TPU machines. It also supports automatic differentiation for gradient-based model learning. While other libraries exist for state space modeling in Python (and some also use `JAX`), this library provides a unique combination of low-level inference algorithms and high-level modeling objects that can support a wide range of research applications.
 
 The API for `Dynamax` is divided into two parts: a set of core, functionally pure, low-level inference algorithms, and a high-level, object oriented module for constructing and fitting probabilistic SSMs. The low-level inference API provides message passing algorithms for several common types of SSMs. For example, `Dynamax` provides `JAX` implementations for:
 
 - Forward-Backward algorithms for discrete-state hidden Markov models (HMMs), 
 - Kalman filtering and smoothing algorithms for linear Gaussian SSMs, 
-- Extended and unscented Kalman filtering and smoothing for nonlinear Gaussian SSMs, and
-- Conditional moment filtering and smoothing algorithms for models with non-Gaussian emissions. 
+- Extended and unscented Kalman filtering and smoothing for nonlinear Gaussian SSMs, 
+- Conditional moment filtering and smoothing algorithms for models with non-Gaussian emissions, and
 - Parallel message passing routines take advantage of GPU or TPU acceleration to perform message passing in sublinear time. 
 
-The high-level model API makes it easy to construct, fit, and inspect HMMs and linear Gaussian SSMs.
+The high-level model API makes it easy to construct, fit, and inspect HMMs and linear Gaussian SSMs. Finally, the online `Dynamax` documentation and tutorials provide a wealth of resources for state space modeling experts and newcomers alike.
 
 `Dynamax` has supported several publications. The low-level API has been used in machine learning research [@zhao2023revisiting; @lee2023switching; @chang2023low]. More sophisticated, special purpose models on top of `Dynamax`, like the Keypoint-MoSeq library for modeling postural dynamics of animals [@weinreb2024keypoint]. Finally, the `Dynamax` tutorials are used as reference examples in a major machine learning textbook [@murphy2023probabilistic].