Fixed some typos

Author: zaxtax

Chapter3_MCMC/IntroMCMC.ipynb

@@ -35,7 +35,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "The previous two chapters hid the inner-mechanics of PyMC, and more generally Monte Carlo Markov Chains (MCMC), from the reader. Simpley, it is not absolutely necessary to know about MCMC to perform Bayesian inference. After all, my computer has an internal kernel, compiler, and RAM that I haven't the slightest idea how they function. But I can still operate on my computer just fine. \n",
+      "The previous two chapters hid the inner-mechanics of PyMC, and more generally Monte Carlo Markov Chains (MCMC), from the reader. Simply, it is not absolutely necessary to know about MCMC to perform Bayesian inference. After all, my computer has an internal kernel, compiler, and RAM that I haven't the slightest idea how they function. But I can still operate on my computer just fine. \n",
       "\n",
       "The reason for including this chapter is three-fold. The first is that any book on Bayesian inference must discuss MCMC. I cannot fight this. Blame the statisticians. Secondly, knowing the process of MCMC gives you insight into whether your algorithm has converged. (Converged to what? We will get to that) Thirdly, we'll understand *why* we are returned thousands of samples from the positerior as a solution, which at first thought can be odd. "
      ]
@@ -220,7 +220,7 @@
       "\n",
       "### Algorithms to perform MCMC\n",
       "\n",
-      "There is a large family of algorithms that perform MCMC. Simplestly, most algorithms can be expressed at a high level as follows: (Mathematical details can be found in the appendix. )\n",
+      "There is a large family of algorithms that perform MCMC. These algorithms can be expressed at a high level as follows: (Mathematical details can be found in the appendix. )\n",
       "\n",
       "    1. Start at current position.\n",
       "    2. Propose moving to a new position (investigate a pebble near you ).\n",