Added introduction chapter. NOt fully written, just a start.

Introduction.ipynb

@@ -0,0 +1,39 @@
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+   "cells": [
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "#Introduction\n",
+      "\n",
+      "The Kalman filter was introduced to the world via papers published in 1958 and 1960 by Rudolph E Kalman. This work built on work by Nobert Wiener. Kalman's early papers were extremely abstract, but researchers quickly realized that the papers described a very practical technique to  filter noisy data. From then until now it has been an ongoing topic of research, and there are many books and papers devoted not only to the basics, but many specializations and extensions to the technique. If you are reading this, you have likely come across some of them.\n",
+      "\n",
+      "If you are like me, you probably find them nearly impenetrable. I find that almost all start with very abstract math, assume familiarity with notation and naming conventions that you probably haven't seen before, and focus heavily on proof rather than exposition and teaching. This is perhaps understandable, but it is a regrettable situation. \n",
+      "\n",
+      "While you do need to know some basic probability and some very basic linear algebra to understand and implement Kalman filters, by and large you really do not need to understand the complicated, multi-page proofs of correctness. The end result is a small handful of equations which you can use to perform very sophisticated filtering, smoothing, and tracking. Implementing the basic equations is never difficult. Kalman filter design is much more an art than a science - implementers spend a few minutes writing the basic equations, and then a lot of time tuning the filter for their specific problem. \n",
+      "\n",
+      "I compare this to a student learning the equation for the volume of a sphere $V(r) = \\frac{4}{3}\\pi r^3$ A student can use this equation without even understanding the functional notation $V(r)$, and certainly not have the calculus knowledge to derive the equation itself. Eventually, in some domains, knowledge of the calculus will become useful, but an enormous amount of work can be done by only knowing the equation and how to apply it.\n",
+      "\n",
+      "I argue the same is true with Kalman filters. I will not prove the equations are correct, nor will I derive them. bvlah blah blah.\n"
+     ]
+    },
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+}

histogram_filter.ipynb

@@ -11,7 +11,17 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "# Introduction to Tracking\n",
+      "#Introduction\n",
+      "\n",
+      "The Kalman filter belongs to a family of filters called *bayesian filters*. Without going into"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "# Tracking a Dog\n",
+      "\n",
       "Let us begin with a simple problem. We have a dog friendly workspace, and so people bring their dogs to work. However, occasionally the dogs wander out of your office and down the halls. We want to be able to track them. So during a hackathon somebody created a little sonar sensor to attach to the dog's collar. It emits a signal, listens for the echo, and based on how quickly an echo comes back we can tell whether the dog is in front of an open doorway or not. It also senses when the dog walks, and reports in which direction the dog has moved. It connects to our network via wifi and sends an update once a second.\n",
       "\n",
       "I want to track my dog Simon, so I attach the device to his collar and then fire up Python, ready to try to write code to track him through the building. At first blush this may appear impossible. If I start listening to the sensor of Simon's collar I might read 'door', 'hall', 'hall', and so on. How can I use that information to determine where Simon is?\n",