premerge with y-p

Author: CamDavidsonPilon

Chapter2_MorePyMC/MorePyMC.ipynb

@@ -753,7 +753,7 @@
      "source": [
       "With regards to the above plot, we are still pretty uncertain about what the true frequency of cheaters might be, but we have narrowed it down to a range between 0.10 to 0.30 (marked by the dashed lines). This is pretty good, as *a priori* we had no idea how many students might have cheated (hence the uniform distribution for our prior). On the other hand, it is also pretty bad since there is a .2 length window the true value most likely lives in. Have we even gained anything, or are we still too uncertain about the true frequency? \n",
       "\n",
-      "I would argue, yes, we have discovered something. It is implausbile, according to our posterior that there are *no cheaters*, i.e. the posterior assigns low probability probability to $p=0$. Since started with a uniform prior, so we gave every $p$ a plausible chance, but the data ruled out $p=0$ as a possibility. We can be confident that there were cheaters. \n",
+      "I would argue, yes, we have gained something. It is implausbile, according to our posterior, that there are *no cheaters*, i.e. the posterior assigns low probability probability to $p=0$. Starting with a uniform prior, we gave every possible $p$ a plausible chance, but the data ruled out $p=0$ as a possibility. We can be confident that there were cheaters. \n",
       "\n",
       "This kind of algorithm can be used to gather private information from users and be *reasonablly* confident that the data, though noisy, is truthful. \n"
      ]

Chapter3_MCMC/IntroMCMC.ipynb

@@ -96,6 +96,7 @@ Thanks to all our contributing authors, including (in chronological order):
 -  [Paul Magwene](http://github.com/pmagwene) 
 -  [Matthias Bussonnier](https://github.com/Carreau)
 -  [Jens Rantil](https://github.com/JensRantil)
+-  [y-p](https://github.com/y-p)
 
 
 We would like to thank the Python community for building an amazing architecture. We would like to thank the