diff --git a/Chapter02_Discrete_Bayes/discrete_bayes.ipynb b/Chapter02_Discrete_Bayes/discrete_bayes.ipynb
index 271a2a2..c0c6732 100644
--- a/Chapter02_Discrete_Bayes/discrete_bayes.ipynb
+++ b/Chapter02_Discrete_Bayes/discrete_bayes.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:44cac9d4e1b88b5a67224fee08c36f0fe84b55edfaf8f1ab7a1c996d083a36d8"
+  "signature": "sha256:8c25e49f13c73dcadcb995b7316539e0f420423df6a8583eb5ec8e7a3f0baf6d"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -1173,7 +1173,7 @@
       "Finally, the bar charts may strike you as being a bit less certain than we would want. A 25% certaintly may not give you a lot of confidence in the anwser. Of course, what is important here is the ratio of this probability to the other probabilities in your vector. If the next largest bar is 23% then we are not very knowledgable about our position, whereas if the next largest is 3% we are in fact quite certain. But this is not clear or intuitive. However, there is an extremely important insight that Kalman filters implement that will signficantly improve our accuracy from the same data.\n",
       "\n",
       "\n",
-      "**If you can understand this chapter you will be able to understand and implement Kalman filters** I cannot stress this enough. If anything is murky, go back and reread this chapter and play with the code. the rest of this book will build on the algorithms that we use here. If you don't intuitively understand why this histogram filter works, and can at least work through the math, you will have little success with the rest of the material. However, if you grasp the fundamental insight - multiplying probabilities when we measure, and shifting probabilities when we update leads to a converging solution - then you understand everything important you need to grasp the Kalman filter. "
+      "**If you can understand this chapter you will be able to understand and implement Kalman filters** I cannot stress this enough. If anything is murky, go back and reread this chapter and play with the code. The rest of this book will build on the algorithms that we use here. If you don't intuitively understand why this histogram filter works, and can at least work through the math, you will have little success with the rest of the material. However, if you grasp the fundamental insight - multiplying probabilities when we measure, and shifting probabilities when we update leads to a converging solution - then you understand everything important you need to grasp the Kalman filter. "
      ]
     },
     {