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@@ -123,7 +123,7 @@ A lengthier discussion of the `EPCA` constructors and math is provided in the [d
 
 # Applications
 
-The practical applications of `ExpFamilyPCA.jl` span several domains that deal with non-Gaussian data. One notable example is in reinforcement learning, specifically in belief state compression for partially observable Markov decision processes (POMDPs). Using Poisson EPCA, the package effectively reduces high-dimensional belief spaces with minimal information loss, as demonstrated by recreating @shortRoy results. In this case, Poisson EPCA achieved nearly perfect reconstruction of a $41$-dimensional belief profile using just five basis components [CITE `CompressedBeleifMDPS.jl`].
+The practical applications of `ExpFamilyPCA.jl` span several domains that deal with non-Gaussian data. One notable example is in reinforcement learning, specifically in belief state compression for partially observable Markov decision processes (POMDPs). Using Poisson EPCA, the package effectively reduces high-dimensional belief spaces with minimal information loss, as demonstrated by recreating @shortRoy results. In this case, Poisson EPCA achieved nearly perfect reconstruction of a $41$-dimensional belief profile using just five basis components [CITE `CompressedBeleifMDPS.jl`, PAPER IN PRE-REVIEW].
 
 ![](./scripts/kl_divergence_plot.png)