diff --git a/README.Rmd b/README.Rmd
index 07d1b01..22a0c06 100644
--- a/README.Rmd
+++ b/README.Rmd
@@ -38,7 +38,8 @@ $$
 Y_i = \beta_0 + \beta_x X_i + \beta_z Z_i + \epsilon_i, \quad \epsilon_i \stackrel{iid}{\sim} N(0, \sigma^2_Y), \quad i=1,\dots,n,
 $$
 
-In the context of environmental epidemiology, the covariate $X_i$ can be an exposure to air pollution of subject $i$ at different locations, $Z_i$ can be demographic information, and $Y_i$ can be the associated health outcome. Since exposure $X_i$, $i=1,\dots,n$ are not directly measured at health subject locations but are predicted from air pollution monitoring station locations, this induces spatially correlated measurement error in $X_i$. Also, the uncertainty information should be taken account, which depends on the proximity of the monitoring station to the subject location. One way to incorporate this information is to use a multivariate normal prior on the covariate $(X_1,\dots,X_n)$, 
+In the context of environmental epidemiology, the covariate $X_i$ can be an exposure to air pollution of subject $i$ at different locations, $Z_i$ can be demographic information, and $Y_i$ can be the associated health outcome. Since exposure $X_i$, $i=1,\dots,n$ are not directly measured at health subject locations but are predicted from air pollution monitoring station locations, this induces spatially correlated measurement error in $X_i$. Also, the uncertainty information should be taken account, which depends on the proximity of the monitoring station to the subject location. One way to incorporate this information is to use a multivariate normal (MVN) prior on a covariate $(X_1,\dots,X_n)$ for all subjects, 
+
 $$
 (X_1,\dots,X_n)\sim \mathrm{N}_n(\mathbf{m}, \mathbf{Q}^{-1}),
 $$
@@ -62,9 +63,9 @@ devtools::install_github("changwoo-lee/bspme")
 
 | Function | Description                                                              |
 | ---------------------- | -------------------------------------------------------------------------|
-| `blinreg_me()`        | Bayesian normal linear regression models with (spatially) correlated measurement errors                                        |
-| `blogireg_me()`       | Bayesian logistic regression models with (spatially) correlated measurement errors                                    |
-| `vecchia_cov()`       | Perform Vecchia approximation given a MVN covariance matrix               |
+| `blinreg_me()`        | Bayesian normal linear regression models with a MVN prior for measurement errors      |
+| `blogireg_me()`       | Bayesian logistic regression models with a MVN prior for measurement errors                                    |
+| `vecchia_cov()`       | Perform the Vecchia approximation given a MVN covariance matrix               |
 
 
 
diff --git a/README.md b/README.md
index cbbe353..0b2eabd 100644
--- a/README.md
+++ b/README.md
@@ -39,8 +39,10 @@ pollution monitoring station locations, this induces spatially
 correlated measurement error in $X_i$. Also, the uncertainty information
 should be taken account, which depends on the proximity of the
 monitoring station to the subject location. One way to incorporate this
-information is to use a multivariate normal prior on the covariate
-$(X_1,\dots,X_n)$, $$
+information is to use a multivariate normal (MVN) prior on a covariate
+$(X_1,\dots,X_n)$ for all subjects,
+
+$$
 (X_1,\dots,X_n)\sim \mathrm{N}_n(\mathbf{m}, \mathbf{Q}^{-1}),
 $$ with some mean $\mathbf{m}$ and precision (inverse covariance) matrix
 $\mathbf{Q}$, referred as a MVN prior approach.
@@ -70,11 +72,11 @@ devtools::install_github("changwoo-lee/bspme")
 
 ## Functionality
 
-| Function        | Description                                                                             |
-|-----------------|-----------------------------------------------------------------------------------------|
-| `blinreg_me()`  | Bayesian normal linear regression models with (spatially) correlated measurement errors |
-| `blogireg_me()` | Bayesian logistic regression models with (spatially) correlated measurement errors      |
-| `vecchia_cov()` | Perform Vecchia approximation given a MVN covariance matrix                             |
+| Function        | Description                                                                      |
+|-----------------|----------------------------------------------------------------------------------|
+| `blinreg_me()`  | Bayesian normal linear regression models with a MVN prior for measurement errors |
+| `blogireg_me()` | Bayesian logistic regression models with a MVN prior for measurement errors      |
+| `vecchia_cov()` | Perform the Vecchia approximation given a MVN covariance matrix                  |
 
 ## datasets