Merge pull request CamDavidsonPilon#223 from RoyalTS/master

changed slightly confusing phrase

Author: CamDavidsonPilon

Chapter3_MCMC/IntroMCMC.ipynb

@@ -992,7 +992,7 @@
       "\n",
       "The MAP can also be used as a solution to the inference problem, as mathematically it  is the *most likely* value for the unknowns. But as mentioned earlier in this chapter,  this location ignores the uncertainty and doesn't return a distribution.\n",
       "\n",
-      "Typically, it is always a good idea, and rarely a bad idea, to prepend your call to `mcmc` with a call to `MAP(model).fit()`. The intermediate call to `fit` is hardly computationally intensive, and will save you time later due to a shorter burn-in period. \n",
+      "Most often it is a good idea, and rarely a bad idea, to prepend your call to `mcmc` with a call to `MAP(model).fit()`. The intermediate call to `fit` is hardly computationally intensive, and will save you time later due to a shorter burn-in period. \n",
       "\n",
       "#### Speaking of the burn-in period\n",
       "\n",
@@ -1280,7 +1280,7 @@
       "\n",
       "#### Priors\n",
       "\n",
-      "If the priors are poorly chosen, the MCMC algorithm may not converge, or atleast have difficulty converging. Consider what may happen if the prior chosen does not even contain the true parameter: the prior assigns 0 probability to the unknown, hence the posterior will assign 0 probability as well. This can cause pathological results.\n",
+      "If the priors are poorly chosen, the MCMC algorithm may not converge, or at least have difficulty converging. Consider what may happen if the prior chosen does not even contain the true parameter: the prior assigns 0 probability to the unknown, hence the posterior will assign 0 probability as well. This can cause pathological results.\n",
       "\n",
       "For this reason, it is best to carefully choose the priors. Often, lack of covergence or evidence of samples crowding to boundaries implies something is wrong with the chosen priors (see *Folk Theorem of Statistical Computing* below). \n",
       "\n",