diff --git a/tutorials/cont_traits/figures/ou_gm.png b/tutorials/cont_traits/figures/ou_gm.png
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diff --git a/tutorials/cont_traits/figures/rjoubm_gm.png b/tutorials/cont_traits/figures/rjoubm_gm.png
new file mode 100644
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diff --git a/tutorials/cont_traits/modules/relaxed_OU_parameter_estimation.md b/tutorials/cont_traits/modules/relaxed_OU_parameter_estimation.md
index d79ba763..ebde24cb 100644
--- a/tutorials/cont_traits/modules/relaxed_OU_parameter_estimation.md
+++ b/tutorials/cont_traits/modules/relaxed_OU_parameter_estimation.md
@@ -1,6 +1,6 @@
 {% section Relaxing the OU Model %}
 
-Under a simple Ornstein-Uhlenbeck (OU) model, the optimal of a continuous character is determined by a single rate parameter, $\theta$. Many evolutionary questions are related to how the optimal phenotype changes among lineages. In a Bayesian setting, we can specify a ''relaxed'' OU prior model that allows the optimal phenotype to vary over the phylogeny.
+Under a simple Ornstein-Uhlenbeck (OU) model, the optimal of a continuous character is determined by a single location parameter, $\theta$. Many evolutionary questions are related to how the optimal phenotype changes among lineages. In a Bayesian setting, we can specify a ''relaxed'' OU prior model that allows the optimal phenotype to vary over the phylogeny.
 
 Here, we will use the ''random local clock'' model, similar to the one described by {% citet Uyeda2014 %} in the software `bayOU`. In this model, we assume that each branch in the phylogeny either does or does not have a optimum shift. When there is no shift on a branch, the optimum on the branch is inherited directly from its ancestral branch; when there _is_ a shift, the ancestral optimum is shifted by an amount that is drawn from a specified prior distribution. We specify a prior probability, $p$, that a given branch experiences a shift. For a tree with $n$ branches, the expected number of shifts is $E(k) = n \times p$. To control the number of shifts, we specify a prior on the _expected number of shifts_, $E(k)$, and then calculate the prior probability for a shift on a particular branch, $p = E(k) / n$. The graphical model shows the relationship between the branch-specific optima and priors (fig_ou_relaxed_gm).
 
@@ -22,7 +22,8 @@ We begin by deciding which of the traits to use. Here, we assume we are analyzin
 trait <- 1
 ```
 
-Now, we read in the (time-calibrated) tree corresponding.
+Now, we read in the (time-calibrated) tree corresponding and specify the tree model.
+In this tutorial, we assume the tree is known without error. We create a constant node for the tree that corresponds to the observed phylogeny.
 ```
 T <- readTrees("data/primates_tree.nex")[1]
 ```
@@ -52,29 +53,22 @@ monitors = VectorMonitors()
 
 {% subsection Specifying the model %}
 
-{% subsubsection Tree model %}
+{% subsubsection Diffusion parameter %}
 
-In this tutorial, we assume the tree is known without area. We create a constant node for the tree that corresponds to the observed phylogeny.
+The stochastic rate of evolution is controlled by the rate parameter $\sigma^2$, which represents the diffusion variance per unit time. We draw the rate parameter from a log-normal distribution, where the median is the non-phylogenetic across-species variance scaled by the root age. We use $2*H \approx 1.1748$ as the spread parameter such that the 95% interval span $\approx 2$ orders of magnitude.
 
 ```
-tree <- T
-```
-
-{% subsubsection Rate parameter %}
-
-The stochastic rate of evolution is controlled by the rate parameter, $\sigma^2$. We draw the rate parameter from a loguniform prior. This prior is uniform on the log scale, which means that it is represents ignorance about the _order of magnitude_ of the rate. As before, we provide a scale move that proposes changes to the parameter during MCMC.
-
-```
-sigma2 ~ dnLoguniform(1e-3, 1)
+root_age := tree.rootAge()
+sigma2 ~ dnLognormal(ln(data.var(trait) / root_age), 1.1748)
 moves.append( mvScale(sigma2, weight=1.0) )
 ```
 
-{% subsubsection Adaptation parameter %}
+{% subsubsection Attraction rate parameter %}
 
-The rate of adaptation toward the optimum is determined by the parameter $\alpha$. We draw $\alpha$ from an exponential prior distribution, and place a scale proposal on it. This parameter is assumed to be constant across the tree (even though the optimum will vary). We specify the mean of the exponential prior distribution on $\alpha$ to be half the root age divided by $\ln(2)$, which means that we expect a phylogenetic half life of half the tree age.
+The rate of attraction toward the optimum is determined by the parameter $\alpha$. We draw $\alpha$ from a log-normal prior distribution, and place a scale proposal on it. This parameter is assumed to be constant across the tree (even though the optimum will vary). We specify the median of the log-normal prior distribution on $\alpha$ to be $\ln(2)$ divided by half the root age, which means that we expect a phylogenetic half life of half the tree age. We specify the spread parameter to be $2*H \approx 1.1748$ , such that the 95% interval spans $\approx 2$ orders of magnitude.
 ```
 root_age := tree.rootAge()
-alpha ~ dnExponential( abs(root_age / 2.0 / ln(2.0)) )
+alpha ~ dnLognormal( abs(ln(2.0) / (0.5 * root_age)), 1.1748 )
 moves.append( mvScale(alpha, weight=1.0) )
 ```
 
@@ -92,7 +86,7 @@ expected_number_of_shifts <- 5
 shift_probability <- expected_number_of_shifts / nbranches
 ```
 
-Next, we specify the prior distribution on the size of shifts (when they occur). We draw each rate shift from a uniform distribution.
+Next, we specify the prior distribution on the size of shifts (when they occur). We draw each rate shift from a normal distribution.
 ```
 shift_distribution = dnNormal(0, 0.587)
 ```
diff --git a/tutorials/cont_traits/modules/simple_OU_model_selection.md b/tutorials/cont_traits/modules/simple_OU_model_selection.md
index 4e3db57f..befa6b28 100644
--- a/tutorials/cont_traits/modules/simple_OU_model_selection.md
+++ b/tutorials/cont_traits/modules/simple_OU_model_selection.md
@@ -14,12 +14,20 @@ $$
 
 where $P( \text{OU model} \mid X)$ and $P( \text{OU model})$ are the posterior probability and prior probability of the OU model, respectively.
 
+{% figure fig_rjoubm_gm %}
+<img src="figures/rjoubm_gm.png" width="50%" height="50%" />
+{% figcaption %}
+The graphical model representation of the mixture model for Ornstein-Uhlenbeck (OU) and Brownin motion (BM) processes using a reversible-jump algorithm.
+For more information about graphical model representations see {% citet Hoehna2014b %}.
+{% endfigcaption %}
+{% endfigure %}
+
 {% subsubsection Reversible-jump between OU and BM models %}
 
 To enable rjMCMC, we simply have to place a reversible-jump prior on the relevant parameter, $\alpha$. We can modify the prior on `alpha` so that it takes either a constant value of 0, or is drawn from a prior distribution. Finally, we specify a prior probability on the OU model of `p = 0.5`.
 
 ```
-alpha ~ dnReversibleJumpMixture(0.0, dnExponential( abs(root_age / 2.0 / ln(2.0)) ), 0.5)
+alpha ~ dnReversibleJumpMixture(0.0, dnLognormal( abs(ln(2.0) / (0.5 * root_age)), 1.1748 ), 0.5)
 ```
 We then provide a reversible-jump proposal on `alpha` that proposes changes between the two models.
 ```
diff --git a/tutorials/cont_traits/modules/simple_OU_parameter_estimation.md b/tutorials/cont_traits/modules/simple_OU_parameter_estimation.md
index f5b3f75e..2f652972 100644
--- a/tutorials/cont_traits/modules/simple_OU_parameter_estimation.md
+++ b/tutorials/cont_traits/modules/simple_OU_parameter_estimation.md
@@ -22,8 +22,9 @@ trait <- 1
 ```
 
 Now, we read in the (time-calibrated) tree corresponding.
+In this tutorial, we assume the tree is known without error. We create a constant node for the tree that corresponds to the observed phylogeny.
 ```
-T <- readTrees("data/primates_tree.nex")[1]
+tree <- readTrees("data/primates_tree.nex")[1]
 ```
 
 Next, we read in the character data for the same dataset.
@@ -45,22 +46,16 @@ moves    = VectorMoves()
 monitors = VectorMonitors()
 ```
 
-{% subsection Specifying the model %}
+{% subsection Specifying the OU model %}
 
-{% subsubsection Tree model %}
 
-In this tutorial, we assume the tree is known without area. We create a constant node for the tree that corresponds to the observed phylogeny.
+{% subsubsection Diffusion parameter %}
 
-```
-tree <- T
-```
-
-{% subsubsection Rate parameter %}
-
-The stochastic rate of evolution is controlled by the rate parameter, $\sigma^2$. We draw the rate parameter from a loguniform prior. This prior is uniform on the log scale, which means that it is represents ignorance about the _order of magnitude_ of the rate.
+The stochastic rate of evolution is controlled by the rate parameter $\sigma^2$, which represents the diffusion variance per unit time. We draw the rate parameter from a log-normal distribution, where the median is the non-phylogenetic across-species variance scaled by the root age. We use $2*H \approx 1.1748$ as the spread parameter such that the 95% interval span $\approx 2$ orders of magnitude.
 
 ```
-sigma2 ~ dnLoguniform(1e-3, 1)
+root_age := tree.rootAge()
+sigma2 ~ dnLognormal(ln(data.var(trait) / root_age), 1.1748)
 ```
 
 In order to estimate the posterior distribution of $\sigma^2$, we must provide an MCMC proposal mechanism that operates on this node. Because $\sigma^2$ is a rate parameter, and must therefore be positive, we use a scaling move called `mvScale`.
@@ -68,15 +63,25 @@ In order to estimate the posterior distribution of $\sigma^2$, we must provide a
 moves.append( mvScale(sigma2, weight=2.0) )
 ```
 
-{% subsubsection Adaptation parameter %}
+{% subsubsection Attraction rate parameter %}
 
-The rate of adaptation toward the optimum is determined by the parameter $\alpha$. We draw $\alpha$ from an exponential prior distribution, and place a scale proposal on it. We specify the mean of the exponential prior distribution on $\alpha$ to be half the root age divided by $\ln(2)$, which means that we expect a phylogenetic half life of half the tree age.
+The rate of attraction toward the optimum is determined by the parameter $\alpha$. We draw $\alpha$ from a log-normal prior distribution, and place a scale proposal on it. We specify the median of the log-normal prior distribution on $\alpha$ to be $\ln(2)$ divided by half the root age, which means that we expect a phylogenetic half life of half the tree age. We specify the spread parameter to be $2*H \approx 1.1748$ , such that the 95% interval spans $\approx 2$ orders of magnitude.
 ```
-root_age := tree.rootAge()
-alpha ~ dnExponential( abs(root_age / 2.0 / ln(2.0)) )
+alpha ~ dnLognormal( abs(ln(2.0) / (0.5 * root_age)), 1.1748 )
 moves.append( mvScale(alpha, weight=2.0) )
 ```
 
+{% aside Alternative: Prior distributions on phylogenetic half-life and/or stationary variance%}
+
+Alternatively, you can draw the phylogenetic half-life and/or stationary variance from some prior distributions, and transform them to $\alpha$ and $\sigma^2$ respectively using the equations below.
+```
+alpha := ln(2) / t_half
+sigma2 := alpha * 2 * vy
+```
+
+{% endaside %}
+
+
 {% subsubsection Optimum %}
 
 We draw the optimal value from a vague uniform prior ranging from -10 to 10 (you should change this prior if your character is outside of this range). Because this parameter can be positive or negative, we use a slide move to propose changes during MCMC.
@@ -98,7 +103,7 @@ moves.append( avmvn_move )
 
 {% subsubsection Assessing the phylogenetic half-life and decrease in variance due to selection %}
 
-For our OU model, we are going to add two variables which are transformations primarily of the strength of selection $\alpha$.
+For our OU model, we are going to add three variables which are transformations of the rate of attraction $\alpha$ and/or the diffusion variance $\sigma^2$.
 First, we add the phylogenetic half-life $t_{1/2} = \ln(2)/\alpha$, which represents the expected time needed for a trait to cover half the distance between root state and the selective optimum $\theta$.
 ```
 t_half := ln(2) / alpha
@@ -108,6 +113,10 @@ Second, we add the metric $p_{th}$ which represents the percent decrease in trai
 ```
 p_th := 1 - (1 - exp(-2.0*alpha*root_age)) / (2.0*alpha*root_age)
 ```
+Lastly, we add the stationary variance which represents the trait variance at stationarity. Recall that unlike the Brownian motion process, the OU process is stationary, i.e., the variance does not increase indefinitely
+```
+vy := sigma2 / ( 2 * alpha )
+```
 
 {% subsubsection Ornstein-Uhlenbeck model %}
 
diff --git a/tutorials/cont_traits/scripts/mcmc_OU.Rev b/tutorials/cont_traits/scripts/mcmc_OU.Rev
index 6990b2e0..0a011914 100644
--- a/tutorials/cont_traits/scripts/mcmc_OU.Rev
+++ b/tutorials/cont_traits/scripts/mcmc_OU.Rev
@@ -4,7 +4,7 @@
 #                   Ornstein-Uhlenbeck model
 #
 #
-# authors: Michael R. May and Sebastian Höhna
+# authors: Michael R. May, Sebastian Höhna, and Priscilla Lau
 #
 ################################################################################
 
@@ -16,8 +16,8 @@
 # Datasets: Female Mass, Tail Length – Body Length Residuals, Body Length – Mass Residuals, Maximum Age, Sexual Dimorphism, Geographic Range Size, Latitudinal Midpoint, Distance to Continental Centroid, Population Density, Home Range Size, Group Size, Gestation Duration, Litter size
 trait <- 1
 
-### Read in the trees
-T <- readTrees("data/primates_tree.nex")[1]
+### Read in the trees and specify the tree model
+tree <- readTrees("data/primates_tree.nex")[1]
 
 ### Read in the character data
 data <- readContinuousCharacterData("data/primates_cont_traits.nex")
@@ -29,22 +29,16 @@ moves    = VectorMoves()
 monitors = VectorMonitors()
 
 
-##########################
-# Specify the tree model #
-##########################
-
-tree <- T
-
 ##########################
 # Specify the rate model #
 ##########################
 
 root_age := tree.rootAge()
 
-sigma2 ~ dnLoguniform(1e-3, 1)
+sigma2 ~ dnLognormal( ln( data.var(trait) / root_age ), 1.1748 )
 moves.append( mvScale(sigma2, weight=2.0) )
 
-alpha ~ dnExponential( abs(root_age / 2.0 / ln(2.0)) )
+alpha ~ dnLognormal( abs(ln(2.0) / (0.5 * root_age)), 1.1748 )
 moves.append( mvScale(alpha, weight=2.0) )
 
 theta ~ dnUniform(-10, 10)
@@ -61,6 +55,7 @@ moves.append( avmvn_move )
 root_age := tree.rootAge()
 t_half := ln(2) / alpha
 p_th := 1 - (1 - exp(-2.0*alpha*root_age)) / (2.0*alpha*root_age)
+vy := sigma2 / ( 2 * alpha )
 
 
 ##########################
diff --git a/tutorials/cont_traits/scripts/mcmc_OU_RJ.Rev b/tutorials/cont_traits/scripts/mcmc_OU_RJ.Rev
index 44f9c9ab..2571b359 100644
--- a/tutorials/cont_traits/scripts/mcmc_OU_RJ.Rev
+++ b/tutorials/cont_traits/scripts/mcmc_OU_RJ.Rev
@@ -3,7 +3,7 @@
 # RevBayes Example: Model selection between OU and BM
 #
 #
-# authors: Michael R. May and Sebastian Höhna
+# authors: Michael R. May, Sebastian Höhna, and Priscilla Lau
 #
 ################################################################################
 
@@ -14,8 +14,8 @@
 ### Select the trait to analyze
 trait <- 1
 
-### Read in the trees
-T <- readTrees("data/primates_tree.nex")[1]
+### Read in the trees and specify the tree model
+tree <- readTrees("data/primates_tree.nex")[1]
 
 ### Read in the character data
 data <- readContinuousCharacterData("data/primates_cont_traits.nex")
@@ -26,22 +26,17 @@ data.includeCharacter( trait )
 moves    = VectorMoves()
 monitors = VectorMonitors()
 
-##########################
-# Specify the tree model #
-##########################
-
-tree <- T
 
 ##########################
 # Specify the rate model #
 ##########################
 
-sigma2 ~ dnLoguniform(1e-3, 1e1)
+sigma2 ~ dnLognormal( ln( data.var(trait) / root_age ), 1.1748 )
 moves.append( mvScale(sigma2, weight=1.0) )
 
 root_age := tree.rootAge()
 
-alpha ~ dnReversibleJumpMixture(0.0, dnExponential( abs(root_age / 2.0 / ln(2.0)) ), 0.5)
+alpha ~ dnReversibleJumpMixture(0.0, dnLognormal( abs(ln(2.0) / (0.5 * root_age)), 1.1748 ), 0.5)
 moves.append( mvRJSwitch(alpha, weight=1.0) )
 moves.append( mvScale(alpha, weight=1.0) )
 
diff --git a/tutorials/cont_traits/scripts/mcmc_OU_prior.Rev b/tutorials/cont_traits/scripts/mcmc_OU_prior.Rev
index aebe2400..920e3016 100644
--- a/tutorials/cont_traits/scripts/mcmc_OU_prior.Rev
+++ b/tutorials/cont_traits/scripts/mcmc_OU_prior.Rev
@@ -16,8 +16,8 @@
 # Datasets: Female Mass, Tail Length – Body Length Residuals, Body Length – Mass Residuals, Maximum Age, Sexual Dimorphism, Geographic Range Size, Latitudinal Midpoint, Distance to Continental Centroid, Population Density, Home Range Size, Group Size, Gestation Duration, Litter size
 trait <- 1
 
-### Read in the trees
-T <- readTrees("data/primates_tree.nex")[1]
+### Read in the trees and specify the tree model
+tree <- readTrees("data/primates_tree.nex")[1]
 
 ### Read in the character data
 data <- readContinuousCharacterData("data/primates_cont_traits.nex")
@@ -29,22 +29,16 @@ moves    = VectorMoves()
 monitors = VectorMonitors()
 
 
-##########################
-# Specify the tree model #
-##########################
-
-tree <- T
-
 ##########################
 # Specify the rate model #
 ##########################
 
 root_age := tree.rootAge()
 
-sigma2 ~ dnLoguniform(1e-3, 1)
+sigma2 ~ dnLognormal( ln( data.var(trait) / root_age ), 1.1748 )
 moves.append( mvScale(sigma2, weight=2.0) )
 
-alpha ~ dnExponential( abs(root_age / 2.0 / ln(2.0)) )
+alpha ~ dnLognormal( abs(ln(2.0) / (0.5 * root_age)), 1.1748 )
 moves.append( mvScale(alpha, weight=2.0) )
 
 theta ~ dnUniform(-10, 10)
@@ -60,6 +54,7 @@ moves.append( avmvn_move )
 # some useful variable transformations to monitor
 t_half := ln(2) / alpha
 p_th := 1 - (1 - exp(-2.0*alpha*root_age)) / (2.0*alpha*root_age)
+vy := sigma2 / ( 2 * alpha )
 
 
 ##########################
diff --git a/tutorials/cont_traits/scripts/mcmc_relaxed_OU.Rev b/tutorials/cont_traits/scripts/mcmc_relaxed_OU.Rev
index 3a5f09c1..a3c8f61c 100644
--- a/tutorials/cont_traits/scripts/mcmc_relaxed_OU.Rev
+++ b/tutorials/cont_traits/scripts/mcmc_relaxed_OU.Rev
@@ -4,7 +4,7 @@
 #                   Ornstein-Uhlenbeck model.
 #
 #
-# authors: Michael R. May and Sebastian Höhna
+# authors: Michael R. May, Sebastian Höhna, and Priscilla Lau
 #
 ################################################################################
 
@@ -15,8 +15,8 @@
 ### Select the trait to analyze
 trait <- 1
 
-### Read in the trees
-T <- readTrees("data/primates_tree.nex")[1]
+### Read in the trees and specify the tree model
+tree <- readTrees("data/primates_tree.nex")[1]
 ntips     <- T.ntips()
 nbranches <- 2 * ntips - 2
 
@@ -29,23 +29,18 @@ data.includeCharacter( trait )
 moves    = VectorMoves()
 monitors = VectorMonitors()
 
-##########################
-# Specify the tree model #
-##########################
-
-tree <- T
 
 ##########################
 # Specify the rate model #
 ##########################
 
-# specify the rate parameter
-sigma2 ~ dnLoguniform(1e-3, 1)
+# specify the diffusion parameter
+root_age := tree.rootAge()
+sigma2 ~ dnLognormal( ln( data.var(trait) / root_age ), 1.1748 )
 moves.append( mvScale(sigma2, weight=1.0) )
 
-# specify the strength parameter
-root_age := tree.rootAge()
-alpha ~ dnExponential( abs(root_age / 2.0 / ln(2.0)) )
+# specify the attraction rate parameter
+alpha ~ dnLognormal( abs(ln(2.0) / (0.5 * root_age)), 1.1748 )
 moves.append( mvScale(alpha, weight=1.0) )
 
 # specify theta at the root of the tree
diff --git a/tutorials/cont_traits/simple_ou.md b/tutorials/cont_traits/simple_ou.md
index 21e3a769..73172c71 100644
--- a/tutorials/cont_traits/simple_ou.md
+++ b/tutorials/cont_traits/simple_ou.md
@@ -1,7 +1,7 @@
 ---
 title: Simple Ornstein-Uhlenbeck Models
 subtitle: Estimating optima under Ornstein-Uhlenbeck evolution
-authors: Michael R. May and Sebastian Höhna
+authors: Michael R. May, Sebastian Höhna, and Priscilla Lau
 level: 6
 order: 1.7
 prerequisites: