Merge remote-tracking branch 'upstream/master'

Author: elpres

Chapter1_Introduction/Chapter1_Introduction.ipynb

@@ -235,7 +235,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "The posterior probabilities are represented by the curves, and our confidence is proportional to the width of the curve. As the plot above shows, as we start to observe data our posterior probabilities start to shift and move around. Eventually, as we observe more and more data (coin-flips), our probabilities will tighten closer and closer around the true value of $p=0.5$ (marked by a dashed line). \n",
+      "The posterior probabilities are represented by the curves, and our uncertainty is proportional to the width of the curve. As the plot above shows, as we start to observe data our posterior probabilities start to shift and move around. Eventually, as we observe more and more data (coin-flips), our probabilities will tighten closer and closer around the true value of $p=0.5$ (marked by a dashed line). \n",
       "\n",
       "Notice that the plots are not always *peaked* at 0.5. There is no reason it should be: recall we assumed we did not have a prior opinion of what $p$ is. In fact, if we observe quite extreme data, say 8 flips and only 1 observed heads, our distribution would look very biased *away* from lumping around 0.5 (with no prior opinion, how confident would you feel betting on a fair coin after observing 8 tails and 1 head). As more data accumulates, we would see more and more probability being assigned at $p=0.5$, though never all of it.\n",
       "\n",
@@ -566,7 +566,7 @@
       "&\\lambda_2 \\sim \\text{Exp}( \\alpha )\n",
       "\\end{align}\n",
       "\n",
-      "$\\alpha$ is called a *hyper-parameter* or *parent variable*. In literal terms, it is a parameter that influences other parameters. Our initial guess at $\\alpha$ does not influence the model too strongly, so we have some flexibility in our choice.  A good rule of thumb is to set the exponential parameter equal to the inverse of the average of the count data. Since we're modeling $\\\\lambda$ using an exponential distribution, we can use the expected value identity shown earlier to get:\n",
+      "$\\alpha$ is called a *hyper-parameter* or *parent variable*. In literal terms, it is a parameter that influences other parameters. Our initial guess at $\\alpha$ does not influence the model too strongly, so we have some flexibility in our choice.  A good rule of thumb is to set the exponential parameter equal to the inverse of the average of the count data. Since we're modeling $\\lambda$ using an exponential distribution, we can use the expected value identity shown earlier to get:\n",
       "\n",
       "$$\\frac{1}{N}\\sum_{i=0}^N \\;C_i \\approx E[\\; \\lambda \\; |\\; \\alpha ] = \\frac{1}{\\alpha}$$ \n",
       "\n",

Chapter2_MorePyMC/MorePyMC.ipynb

@@ -154,14 +154,6 @@ Reviews
 The publishing model is so unusual. Not only is it open source but it relies on pull requests from anyone in order to progress the book. This is ingenious and heartening" - [excited Reddit user](http://www.reddit.com/r/Python/comments/1alnal/probabilistic_programming_and_bayesian_methods/)
 
 
-#### Mobile? You bet
-
-Available for ALL the mobile devices, as these in real-life screenshots suggest:
-
-![so real](http://i.imgur.com/6jV4mSr.png)
-![really real](http://i.imgur.com/JMdYv6g.png)
-
-(thanks to [http://placeit.breezi.com/](http://placeit.breezi.com/) for these hilarious shots)
 
 Contributions and Thanks
 -----