Update README.md

README.md

@@ -13,7 +13,7 @@ emergent constraints: case studies of climate sensitivity with PMIP. Climate of
 the Past.
 
 The Bayesian method is using the package PyMC3: Salvatier J., Wiecki T.V., 
-Fonnesbeck C. (2016) Probabilistic programming in Python using PyMC3. 
+Fonnesbeck C. (2016). Probabilistic programming in Python using PyMC3. 
 PeerJ Computer Science 2:e55 DOI: 10.7717/peerj-cs.55.
 See https://docs.pymc.io for installation.
 
@@ -23,6 +23,9 @@ https://github.com/Phlya/adjustText for installation.
 The conjugate prior approach is using the package spBayes. Please refer to the package description
 by Finley and Banerjee (2020) https://cran.r-project.org/web/packages/spBayes/spBayes.pdf
 
+The Bayesian method in R is using the package RSTAN: Stan Development Team (2019). 
+“RStan: the R interface to Stan.” R package version 2.19.1, http://mc-stan.org/.
+
 Other used packages are included in the standard Anaconda Distribution.
 
 For any inquiries or technical questions, please contact 
@@ -30,6 +33,10 @@ Martin Renoult (Stockholm University, Bolin Centre for Climate Research and
 Department of Meteorology)
 martin.renoult@misu.su.se
 
+02/07/2020: - Corrected the conjugate prior approach (based on scaled variance) to make
+it consistent with the Bayesian approach
+            - Added an R implementation of the Bayesian approach with RSTAN
+
 21/05/2020: - Corrected a mistake in the computation of the variance L80 of the Kalman Filter
             - Updated the Bayesian approach with more annotations, removed some useless stuff
             - Removed the previous OLS python script

[David Jones]

Step_By_Step_Guide_To_Write_A_Character_Analysis_Essay_3.pdf