Merge pull request CamDavidsonPilon#132 from elpres/master

Small grammar fix

Author: CamDavidsonPilon

Chapter3_MCMC/IntroMCMC.ipynb

@@ -313,7 +313,7 @@
       "\n",
       "If the current position of the MCMC algorithm is in an area of extremely low probability, which is often the case when the algorithm begins (typically at a random location in the space), the algorithm will move in positions *that are likely not from the posterior* but better than everything else nearby. Thus the first moves of the algorithm are not reflective of the posterior.\n",
       "\n",
-      "In the above algorithm's pseudocode, notice that only the current position matters (new positions are investigated only near the current position). We can describe this property as *memorylessness*, i.e. the algorithm does not care *how* it arrived at it's current position, only that it is there. \n",
+      "In the above algorithm's pseudocode, notice that only the current position matters (new positions are investigated only near the current position). We can describe this property as *memorylessness*, i.e. the algorithm does not care *how* it arrived at its current position, only that it is there. \n",
       "\n",
       "### Other approximation solutions to the posterior\n",
       "Besides MCMC, there are other procedures available for determining the posterior distributions. A [Laplace approximation](http://en.wikipedia.org/wiki/Laplace's_method) is an approximation of the posterior using simple functions. A more advanced method is [Variational Bayes](http://en.wikipedia.org/wiki/Variational_Bayesian_methods). All three methods, Laplace Approximations, Variational Bayes, and classical MCMC have their pros and cons. We will only focus on MCMC in this book. That being said, my friend Imri Sofar likes to classify MCMC algorithms as either \"they suck\", or \"they really suck\". He classifies the particular flavour of MCMC used by PyMC as just *sucks* ;)"
@@ -1337,4 +1337,4 @@
    "metadata": {}
   }
  ]
-}
+}