diff --git a/CHANGELOG.md b/CHANGELOG.md
index 8f3411999..79d187df9 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -9,6 +9,22 @@ For more information on HARK, see [our Github organization](https://github.com/e
 
 ## Changes
 
+### 0.10.6
+
+Release Date: 17-04-2020
+
+#### Major Changes
+
+* Add Bellman equations for cyclical model example [#600](https://github.com/econ-ark/HARK/pull/600)
+
+* read_shocks now reads mortality as well [#613](https://github.com/econ-ark/HARK/pull/613)
+
+* Discrete probability distributions are now classes [#610](https://github.com/econ-ark/HARK/pull/610)
+
+#### Minor Changes 
+
+
+
 ### 0.10.5
 
 Release Date: 24-03-2020
diff --git a/HARK/ConsumptionSaving/ConsMarkovModel.py b/HARK/ConsumptionSaving/ConsMarkovModel.py
index ec16f4066..610e999af 100644
--- a/HARK/ConsumptionSaving/ConsMarkovModel.py
+++ b/HARK/ConsumptionSaving/ConsMarkovModel.py
@@ -12,6 +12,7 @@
 from HARK import AgentType
 from HARK.ConsumptionSaving.ConsIndShockModel import ConsIndShockSolver, ValueFunc, \
                              MargValueFunc, ConsumerSolution, IndShockConsumerType
+from HARK.distribution import DiscreteDistribution
 from HARK.simulation import drawUniform
 from HARK.interpolation import CubicInterp, LowerEnvelope, LinearInterp
 from HARK.utilities import CRRAutility, CRRAutilityP, CRRAutilityPP, CRRAutilityP_inv, \
@@ -894,15 +895,13 @@ def getShocks(self):
                 if N > 0:
                     IncomeDstnNow    = self.IncomeDstn[t-1][j] # set current income distribution
                     PermGroFacNow    = self.PermGroFac[t-1][j] # and permanent growth factor
-                    Indices          = np.arange(IncomeDstnNow[0].size) # just a list of integers
+                    Indices          = np.arange(IncomeDstnNow.pmf.size) # just a list of integers
                     # Get random draws of income shocks from the discrete distribution
-                    EventDraws       = DiscreteDistribution(
-                        IncomeDstnNow[0], Indices
-                    ).drawDiscrete(
+                    EventDraws       = IncomeDstnNow.draw_events(
                         N,
                         seed=self.RNG.randint(0,2**31-1))
-                    PermShkNow[these] = IncomeDstnNow[1][EventDraws]*PermGroFacNow # permanent "shock" includes expected growth
-                    TranShkNow[these] = IncomeDstnNow[2][EventDraws]
+                    PermShkNow[these] = IncomeDstnNow.X[0][EventDraws]*PermGroFacNow # permanent "shock" includes expected growth
+                    TranShkNow[these] = IncomeDstnNow.X[1][EventDraws]
         newborn = self.t_age == 0
         PermShkNow[newborn] = 1.0
         TranShkNow[newborn] = 1.0
diff --git a/HARK/__init__.py b/HARK/__init__.py
index c4f4544c7..48fdbac57 100644
--- a/HARK/__init__.py
+++ b/HARK/__init__.py
@@ -1,3 +1,3 @@
 from .core import *
 
-__version__ = "0.10.5"
+__version__ = "0.10.6"
diff --git a/README.md b/README.md
index 3f46a9f6c..79fd32086 100644
--- a/README.md
+++ b/README.md
@@ -1,6 +1,6 @@
 <div align="center">
   <a href="https://econ-ark.org">
-    <img src="doc/images/econ-ark-logo.png" align="center">
+    <img src="https://econ-ark.org/assets/img/econ-ark-logo.png" align="center">
   </a>
   <br>
   <br>
@@ -39,7 +39,7 @@ The Econ-ARK project uses an [open governance model](./GOVERNANCE.md) and is fis
 
 <div align="center">
   <a href="https://numfocus.org/project/econ-ark">
-    <img height="60px" src="doc/images/numfocus-logo.png" align="center">
+    <img height="60px" src="https://numfocus.org/wp-content/uploads/2018/01/optNumFocus_LRG.png" align="center">
   </a>
 </div>
 <br>
@@ -82,7 +82,7 @@ Install from [PyPi](https://pypi.org/) by running:
 We start with almost the simplest possible consumption model: A consumer with CRRA utility 
 
 <div align="center">
-  <img height="30px" src="doc/images/usage-crra-utility-function.png">
+  <img height="30px" src="https://github.com/econ-ark/HARK/blob/master/doc/images/usage-crra-utility-function.png">
 </div> 
 
 has perfect foresight about everything except the (stochastic) date of death.
@@ -90,7 +90,7 @@ has perfect foresight about everything except the (stochastic) date of death.
 The agent's problem can be written in [Bellman form](https://en.wikipedia.org/wiki/Bellman%E2%80%93Ford_algorithm) as:
 
 <div align="center">
-  <img height="80px" src="doc/images/usage-agent-problem-bellman-form.png">
+  <img height="80px" src="https://github.com/econ-ark/HARK/blob/master/doc/images/usage-agent-problem-bellman-form.png">
 </div>
 
 <br>
diff --git a/setup.cfg b/setup.cfg
index e2fd57628..1a13dd9c9 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -1,6 +1,6 @@
 [metadata]
 name = econ-ark
-version = 0.10.5
+version = 0.10.6
 url = https://github.com/econ-ark/HARK
 project_urls =
         Bug Reports = https://github.com/econ-ark/HARK/issues