Fixed some wording.

README.md

@@ -4,7 +4,7 @@
 Introductory text for Kalman and Bayesian filters. All code is written in Python, and the book itself is written in IPython Notebook (now known as Jupyter) so that you can run and modify the code in the book in place, seeing the results inside the book. What better way to learn?
 
 
-**"Kalman and Bayesian Filters in Python" looks amazing! ... your book is just what I needed** - Allen Downey, Professor and O'Reilly author of several math and programming textbooks, on twitter.
+**"Kalman and Bayesian Filters in Python" looks amazing! ... your book is just what I needed** - Allen Downey, Professor and O'Reilly author of several math and programming textbooks, via twitter.
 
 
 ![alt tag](https://raw.githubusercontent.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/animations/05_dog_track.gif)
@@ -13,13 +13,13 @@ Introductory text for Kalman and Bayesian filters. All code is written in Python
 Motivation
 -----
 
-This is a book for programmers that have a need or interest in Kalman filtering. The motivation for this book came out of my desire for a gentle introduction to Kalman filtering. I'm a software engineer that spent almost two decades in the avionics field, and so I have always been 'bumping elbows' with the Kalman filter, but never implemented one myself. As I moved into solving tracking problems with computer vision the need became urgent. There are classic textbooks in the field, such as Grewal and Andrew's excellent *Kalman Filtering*. But sitting down and trying to read many of these books is a dismal and trying experience if you do not have the background. Typically the first few chapters fly through several years of undergraduate math, blithely referring you to textbooks on, for example, Itō calculus, and presenting an entire semester's worth of statistics in a few brief paragraphs. These books are good textbooks for an upper undergraduate course, and an invaluable reference to researchers and professionals, but the going is truly difficult for the more casual reader. Symbology is introduced without explanation, different texts use different words and variables names for the same concept, and the books are almost devoid of examples or worked problems. I often found myself able to parse the words and comprehend the mathematics of a definition, but had no idea as to what real world phenomena these words and math were attempting to describe. "But what does that *mean?*" was my repeated thought.
+This is a book for programmers that have a need or interest in Kalman filtering. The motivation for this book came out of my desire for a gentle introduction to Kalman filtering. I'm a software engineer that spent almost two decades in the avionics field, and so I have always been 'bumping elbows' with the Kalman filter, but never implemented one myself. As I moved into solving tracking problems with computer vision the need became urgent. There are classic textbooks in the field, such as Grewal and Andrew's excellent *Kalman Filtering*. But sitting down and trying to read many of these books is a dismal experience if you do not have the required background. Typically the first few chapters fly through several years of undergraduate math, blithely referring you to textbooks on topics such as Itō calculus, and present an entire semester's worth of statistics in a few brief paragraphs. They are good texts for an upper undergraduate course, and an invaluable reference to researchers and professionals, but the going is truly difficult for the more casual reader. Symbology is introduced without explanation, different texts use different terms and variables for the same concept, and the books are almost devoid of examples or worked problems. I often found myself able to parse the words and comprehend the mathematics of a definition, but had no idea as to what real world phenomena they describe. "But what does that *mean?*" was my repeated thought.
 
 However, as I began to finally understand the Kalman filter I realized the underlying concepts are quite straightforward. A few simple probability rules, some intuition about how we integrate disparate knowledge to explain events in our everyday life and the core concepts of the Kalman filter are accessible. Kalman filters have a reputation for difficulty, but shorn of much of the formal terminology the beauty of the subject and of their math became clear to me, and I fell in love with the topic. 
 
-As I began to understand the math and theory more difficulties itself. A book or paper's author makes some statement of fact and presents a graph as proof.  Unfortunately, why the statement is true is not clear to me, nor is the method for making that plot obvious. Or maybe I wonder "is this true if R=0?"  Or the author provides pseudocode - at such a high level that the implementation is not obvious. Some books offer Matlab code, but I do not have a license to that expensive package. Finally, many books end each chapter with many useful exercises. Exercises which you need to understand if you want to implement Kalman filters for yourself, but exercises with no answers. If you are using the book in a classroom, perhaps this is okay, but it is terrible for the independent reader. I loathe that an author withholds information from me, presumably to avoid 'cheating' by the student in the classroom.
+As I began to understand the math and theory more difficulties present themselves. A book or paper's author makes some statement of fact and presents a graph as proof.  Unfortunately, why the statement is true is not clear to me, nor is the method for making that plot obvious. Or maybe I wonder "is this true if R=0?"  Or the author provides pseudocode at such a high level that the implementation is not obvious. Some books offer Matlab code, but I do not have a license to that expensive package. Finally, many books end each chapter with many useful exercises. Exercises which you need to understand if you want to implement Kalman filters for yourself, but exercises with no answers. If you are using the book in a classroom, perhaps this is okay, but it is terrible for the independent reader. I loathe that an author withholds information from me, presumably to avoid 'cheating' by the student in the classroom.
 
-None of this necessary, from my point of view. Certainly if you are designing a Kalman filter for a aircraft or missile you must thoroughly master of all of the mathematics and topics in a typical Kalman filter textbook. I just want to track an image on a screen, or write some code for my Arduino project. I want to know how the plots in the book are made, and chose different parameters than the author chose. I want to run simulations. I want to inject more noise in the signal and see how a filter performs. There are thousands of opportunities for using Kalman filters in everyday code, and yet this fairly straightforward topic is the provenance of rocket scientists and academics.
+From my point of view none of this necessary. Certainly if you are designing a Kalman filter for a aircraft or missile you must thoroughly master of all of the mathematics and topics in a typical Kalman filter textbook. I just want to track an image on a screen, or write some code for an Arduino project. I want to know how the plots in the book are made, and chose different parameters than the author chose. I want to run simulations. I want to inject more noise in the signal and see how a filter performs. There are thousands of opportunities for using Kalman filters in everyday code, and yet this fairly straightforward topic is the provenance of rocket scientists and academics.
 
 I wrote this book to address all of those needs. This is not the book for you if you program navigation computers for Boeing or design radars for Raytheon. Go get an advanced degree at Georgia Tech, UW, or the like, because you'll need it. This book is for the hobbiest, the curious, and the working engineer that needs to filter or smooth data. 
 
@@ -33,7 +33,7 @@ Finally, this book is free. The cost for the books required to learn Kalman filt
 
 In Development
 --------------
-This book is still very much in development.
+This book is still very much in development. Chapters are incomplete, code and text could be incorrect. I strive for correctness during development, but I fail. Raise an issue on GitHub, or email me, if you find a problem and I will try to fix it as soon as possible.
 
 ## Reading Online
 
@@ -98,7 +98,7 @@ Follow the instructions in **Installation and Software Requirements** below to i
 
 This will open a browser window showing the contents of the base directory. The book is organized into chapters. To read Chapter 2, click on the link for chapter 2. This will cause the browser to open that subdirectory. In each subdirectory there will be one or more IPython Notebooks (all notebooks have a .ipynb file extension). The chapter contents are in the notebook with the same name as the chapter name. There are sometimes supporting notebooks for doing things like generating animations that are displayed in the chapter. These are not intended to be read by the end user, but of course if you are curious as to how an animation is made go ahead and take a look.
 
-This is admittedly a somewhat cumbersome interface to a book; I am following in the footsteps of several other projects that are somewhat repurposing IPython Notebook to generate entire books. I feel the slight annoyances have a huge payoff - instead of having to download a separate code base and run it in an IDE while you try to read a book, all of the code and text is in one place. If you want to alter the code, you may do so and immediately see the effects of your change. If you find a bug, you can make a fix, and push it back to my repository so that everyone in the world benefits. And, of course, you will never encounter a problem I face all the time with traditional books - the book and the code are out of sync with each other, and you are left scratching your head as to which source to trust.
+This is admittedly a somewhat cumbersome interface to a book; I am following in the footsteps of several other projects that are somewhat repurposing Jupyter Notebook to generate entire books. I feel the slight annoyances have a huge payoff - instead of having to download a separate code base and run it in an IDE while you try to read a book, all of the code and text is in one place. If you want to alter the code, you may do so and immediately see the effects of your change. If you find a bug, you can make a fix, and push it back to my repository so that everyone in the world benefits. And, of course, you will never encounter a problem I face all the time with traditional books - the book and the code are out of sync with each other, and you are left scratching your head as to which source to trust.
 
 **Breaking change: I have upgraded to IPython 3.0. This release alters the notebook format (.ipynb) files. If you are running an older version you will likely get the unhelpful error message "Bad request" when you try to open the notebook. Note that this is the version number for _IPython_, which provides the IPython Notebook software, and not the Python version. I.e. you can run these notebooks with Python 2.7, so long as you have IPython 3.0 installed. IPython 3.0 was released on Febuary 27, 2015, so if your install is later than that you will have to update IPython. IPython 2.4 can read the files, but not write them. I apologize if you are using an earlier version, but this is an unavoidable change and I'd rather change now instead of later. This will not affect you if you are reading online, only if you are running the notebooks on your local computer. Please note that this has nothing to do with Python 3 - you can run Python 2.7 in IPython 3.**