Add more papers to LITERATURE.md

LITERATURE.md

@@ -5,24 +5,53 @@ Could be moved to `docs/source` eventually.
 
 ## Papers
 
-### SIES
+### SIES (Subspace Iterative Ensemble Smoother)
 
-- (2019) [Efficient Implementation of an Iterative Ensemble Smoother for Data Assimilation and Reservoir History Matching](https://www.frontiersin.org/articles/10.3389/fams.2019.00047/full) by Evensen et al
+An optimization approach.
+The observations are perturbed once, and we use the Gauss-Newton algorithm to minimize a cost function.
+The proposed algorithm has state across iterations in a matrix $W$, which makes it more challenging to reason about compared to ESMDA.
 
-### ESMDA
+- (2019) [**Efficient Implementation of an Iterative Ensemble Smoother for Data Assimilation and Reservoir History Matching**](https://www.frontiersin.org/articles/10.3389/fams.2019.00047/full) by Evensen et al
+
+### ESMDA (Ensemble Smoother with Multiple Data Assimilation)
+
+The idea behind ESMDA is to inflate the covariance of the observations and update several times.
+In the Gauss-Linear case, this makes no difference.
+The hope is that for non-linear forward models, it's better to make many small updates rather than one big update.
+Just like with SIES, there are no theoretical guarantees.
+One difference between ESMDA and SIES is that ESMDA perturbs in each iteration, whereas SIES perturbs once.
+The 2013 paper is the main paper, but the others are related too.
 
 - (2012) [History matching time-lapse seismic data using the ensemble Kalman filter with multiple data assimilations](https://link.springer.com/article/10.1007/s10596-012-9275-5) by Emerick et al
-- (2013) [Ensemble smoother with multiple data assimilation](https://www.sciencedirect.com/science/article/abs/pii/S0098300412000994) by Emerick et al
+- (2013) [**Ensemble smoother with multiple data assimilation**](https://www.sciencedirect.com/science/article/abs/pii/S0098300412000994) by Emerick et al
+- (2016) [An Adaptive Ensemble Smoother With Multiple Data Assimilation for Assisted History Matching](https://doi.org/10.2118/173214-PA) by Emerick et al
+
+### Ensemble Smoothers and related
+
+Some papers that might be useful to have a look at.
+
+- (2012) [Levenberg–Marquardt forms of the iterative ensemble smoother for efficient history matching and uncertainty quantification](https://link.springer.com/article/10.1007/s10596-013-9351-5) by Chen et al
+- (2014) [Randomize-Then-Optimize: A Method for Sampling from Posterior Distributions in Nonlinear Inverse Problems](https://epubs.siam.org/doi/10.1137/140964023) by Bardsley et al
+- (2018) [Analysis of iterative ensemble smoothers for solving inverse problems](https://link.springer.com/article/10.1007/s10596-018-9731-y) by Evensen
+- (2023) [Review of ensemble gradients for robust optimisation](https://arxiv.org/abs/2304.12136) by Raanes et al
 
 ### Covariance regularization
 
+- (2014) [Nonparametric Stein-type Shrinkage Covariance Matrix Estimators in High-Dimensional Settings](https://arxiv.org/abs/1410.4726) by Touloumis
 - (2022) [GraphSPME: Markov Precision Matrix Estimation and Asymptotic Stein-Type Shrinkage](https://arxiv.org/abs/2205.07584) by Lunde et al
 
+### Ensemble Information Filter and related
+
+- (unpublished) An Ensemble Information Filter: Retrieving Markov-Information from the SPDE discretization by Lunde et al
+- (2022) [Ensemble transport smoothing. Part I: Unified framework](https://arxiv.org/abs/2210.17000) by Ramgraber et al
+- (2022) [Ensemble transport smoothing. Part II: Nonlinear updates](https://arxiv.org/abs/2210.17435) by Ramgraber et al
+
 ## Books
 
 ### Preliminaries
 
 - (2006) [Pattern Recognition and Machine Learning](https://www.amazon.com/Pattern-Recognition-Learning-Information-Statistics/dp/0387310738) by Bishop
+    - In particular, see Section 2.3.3: Bayes' theorem for Gaussian variables
 
 ### Data Assimilation