diff --git a/00_Preface.ipynb b/00_Preface.ipynb
index 72324d4..39eb20f 100644
--- a/00_Preface.ipynb
+++ b/00_Preface.ipynb
@@ -255,11 +255,38 @@
     "book_format.load_style()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/javascript": [
+       "IPython.load_extensions('calico-spell-check');\n",
+       "IPython.load_extensions('calico-document-tools');"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%%javascript\n",
+    "IPython.load_extensions('calico-spell-check');\n",
+    "IPython.load_extensions('calico-document-tools');"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Introductory textbook for Kalman filters and Bayesian filters. All code is written in Python, and the book itself is written in Ipython Notebook 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?\n",
+    "Introductory textbook for Kalman filters and Bayesian filters. All code is written in Python, and the book itself is written in IPython Notebook 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?\n",
     "\n",
     "<img src=\"https://raw.githubusercontent.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/animations/05_dog_track.gif\">\n"
    ]
@@ -394,9 +421,9 @@
    "source": [
     "** author's note**. *The book is still being written, and so I am not focusing on issues like supporting multiple versions of Python. I am staying more or less on the bleeding edge of Python 3 for the time being. If you follow my suggestion of installing Anaconda all of the versioning problems will be taken care of for you, and you will not alter or affect any existing installation of Python on your machine. I am aware that telling somebody to install a specific packaging system is not a long term solution, but I can either focus on endless regression testing for every minor code change, or work on delivering the book, and then doing one sweep through it to maximize compatibility. I opt for the latter. In the meantime I welcome bug reports if the book does not work on your platform.*\n",
     "\n",
-    "If you want to run the notebook on your computer, which is what I recommend, then you will have to have IPython installed. I do not cover how to do that in this book; requirements change based on what other python installations you may have, whether you use a third party package like Anaconda Python, what operating system you are using, and so on. \n",
+    "If you want to run the notebook on your computer, which is what I recommend, then you will have to have IPython 2.4 or later installed. I do not cover how to do that in this book; requirements change based on what other python installations you may have, whether you use a third party package like Anaconda Python, what operating system you are using, and so on.\n",
     "\n",
-    "To use all features you will have to have Ipython 2.0 installed, which is released and stable as of April 2014. Most of the book does not require that recent of a version, but I do make use of the interactive plotting widgets introduced in this release. A few cells will not run if you have an older version installed. This is merely a minor annoyance.\n",
+    "The notebook format was changed as of IPython 3.0. If you are running 2.4 you will still be able to open and run the notebooks, but they will be downconverted for you. If you make changes DO NOT push 2.4 version notebooks to me! I strongly recommend updating to 3.0 as soon as possible, as this format change will just become more frustrating with time.\n",
     "\n",
     "You will need Python 2.7 or later installed. Almost all of my work is done in Python 3.4, but I periodically test on 2.7. I do not promise any specific check in will work in 2.7 however. I do use Python's \"from __future__ import ...\" statement to help with compatibility. For example, all prints need to use parenthesis. If you try to add, say, \"print 3.14\" into the book your script will fail; you must write \"print (3.4)\" as in Python 3.X.\n",
     "\n",
@@ -420,6 +447,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "\n",
     "I am writing an open source Bayesian filtering Python library called **FilterPy**. It is available on github at (https://github.com/rlabbe/filterpy). To ensure that you have the latest release you will want to grab a copy from github, and follow your Python installation's instructions for adding it to the Python search path.\n",
     "\n",
     "I have also made the project available on PyPi, the Python Package Index. I will be honest, I am not updating this as fast as I am changing the code in the library. That will change as the library and this book mature. To install from PyPi, at the command line issue the command\n",
diff --git a/05_Kalman_Filters.ipynb b/05_Kalman_Filters.ipynb
index a19e2c2..25052f4 100644
--- a/05_Kalman_Filters.ipynb
+++ b/05_Kalman_Filters.ipynb
@@ -257,6 +257,29 @@
     "book_format.load_style()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import IPython\n",
+    "IPython.html.nbextensions.check_nbextension('gist.js')"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
diff --git a/07_Kalman_Filter_Math.ipynb b/07_Kalman_Filter_Math.ipynb
index cc152af..3550d4c 100644
--- a/07_Kalman_Filter_Math.ipynb
+++ b/07_Kalman_Filter_Math.ipynb
@@ -369,7 +369,7 @@
     "\n",
     "It's the same thing being calculated by the code. Multiply the prior ($P(A)$) by the probability of the measurement at each position ($P(B|A)$) and divide by the total probability for the event ($P(B)$).\n",
     "\n",
-    "In other words the first half of the Discrete Bayes chapter developed Bayes' equation from a thought experiment. I could have just presented Bayes' equation and then given you the Python routine above to implement it, but chances are you would not have understood *why* Bayes' equation works. Presenting the equation first is the normal approach of Kalman filtering texts, and I always found it extremely nonintuitive. \n"
+    "In other words the first half of the Discrete Bayes chapter developed Bayes' equation from a thought experiment. I could have just presented Bayes' equation and then given you the Python routine above to implement it, but chances are you would not have understood *why* Bayes' equation works. Presenting the equation first is the normal approach of Kalman filtering texts, and I always found it extremely nonintuitive. "
    ]
   },
   {
@@ -458,7 +458,6 @@
     "\n",
     "where $\\dot{\\mathbf{x}}$ is the notation for the derivative of $\\mathbf{x}$ with respect to t.\n",
     "\n",
-    "\n",
     "We need to use these equations for the predict step of the Kalman filter. Given the state of the system at time $t$ we want to predict its state at time $t + \\Delta t$. The Kalman filter matrices do not accept differential equations, so we need a mathematical technique that will find the solution to those equations at each time step. In general it is extremely difficult to find analytic solutions to systems of differential equations, so we will normally use *numerical* techniques to find accurate approximations for these equations."
    ]
   },
@@ -719,412 +718,403 @@
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       ],
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fce1bd1cb70>"
+       "<matplotlib.figure.Figure at 0x7f80a140a6a0>"
       ]
      },
      "metadata": {},
@@ -1250,8 +1240,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Continuous White Noise Model\n",
-    "\n",
+    "### Continuous White Noise Model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
     "We model kinematic systems using Newton's equations. So far in this book we have either used position and velocity, or position,velocity, and acceleration as the models for our systems. There is nothing stopping us from going further - we can model jerk, jounce, snap, and so on. We don't do that normally because adding terms beyond the dynamics of the real system actually degrades the solution. \n",
     "\n",
     "Let's say that we need to model the position, velocity, and acceleration. We can then assume that acceleration is constant. Of course, there is process noise in the system and so the acceleration is not actually constant. In this section we will assume that the acceleration changes by a continuous time zero-mean white noise $w(t)$. In other words, we are assuming that velocity is acceleration changing by small amounts that over time average to 0 (zero-mean). \n",
@@ -1332,7 +1327,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "For completeness, let us compute the equations for the 0th order and 1st order equations.\n"
+    "For completeness, let us compute the equations for the 0th order and 1st order equations."
    ]
   },
   {
@@ -1796,7 +1791,7 @@
    "source": [
     "This works for linear ordinary differential equations (ODEs), but does not work (well) for nonlinear equations. For example, consider trying to predict the position of a rapidly turning car. Cars turn by pivoting the front wheels, which cause the car to pivot around the rear axle. Therefore the path will be continuously varying and a linear prediction will necessarily produce an incorrect value. If the change in the system is small enough relative to $\\Delta t$ this can often produce adequate results, but that will rarely be the case with the nonlinear Kalman filters we will be studying in subsequent chapters. Another problem is that even trivial systems produce differential equations for which finding closed form solutions is difficult or impossible. \n",
     "\n",
-    "For these reasons we need to know how to numerically integrate differential equations. This can be a vast topic, and scipy provides integration routines such as `scipy.integrate.ode`. These routines are robust, but "
+    "For these reasons we need to know how to numerically integrate differential equations. This can be a vast topic, and SciPy provides integration routines such as `scipy.integrate.ode`. These routines are robust, but "
    ]
   },
   {
@@ -1817,6 +1812,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "\n",
     "Runge Kutta integration is the workhorse of numerical integration. As mentioned earlier there are a vast number of methods and literature on the subject. In practice, using the Runge Kutta algorithm that I present here will solve most any problem you will face. It offers a very good balance of speed, precision, and stability, and it is used in vast amounts of scientific software. \n",
     "\n",
     "Let's just dive in. We start with some differential equation\n",
@@ -1892,360 +1888,341 @@
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+       "foOTcEB19QAAAABJRU5ErkJggg==\n"
       ],
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fce1a2d0fd0>"
+       "<matplotlib.figure.Figure at 0x7f807e4aa160>"
       ]
      },
      "metadata": {},
@@ -2301,7 +2278,7 @@
    "source": [
     "A broad category of use for the Kalman filter is *sensor fusion*. For example, we might have a position sensor and a velocity sensor, and we want to combine the data from both to find an optimal estimate of state. In this section we will discuss a different case, where we have multiple sensors providing the same type of measurement. \n",
     "\n",
-    " The Global Positioning System (GPS) is designed so that at least 6 satellites are in view at any time at any point on the globe. The GPS receiver knows the location of the satellites in the sky relative to the Earth. At each epoch (instant in time) the receiver gets a signal from each satellite from which it can derive the *pseudorange* to the satellite. In more detail, the GPS receiver gets a signal identifying the satellite along with the time stamp of when the signal was transmitted. The GPS satellite has an atomic clock onboard so this timestamp is extremely accurate. The signal travels at the speed of light, which is constant in a vacuum, so in theory the GPS should be able to produce an extremely accurate distance measurement to the measurement by measuring how long the signal took to reach the receiver. There are several problems with that. First, the signal is not travelling through a vacuum, but through the atmosphere. The atmosphere causes the signal to bend, so it is not travelling in a straight line. This causes the signal to take longer to reach the receiver than theory suggests. Second, the on board clock on the GPS *receiver* is not very accurate, so deriving an exact time duration is nontrivial. Third, in many environments the signal can bounce off of buildings, trees, and other objects, causing either a longer path or *multipaths*, in which case the receive receives both the original signal from space and the reflected signals. \n",
+    " The Global Positioning System (GPS) is designed so that at least 6 satellites are in view at any time at any point on the globe. The GPS receiver knows the location of the satellites in the sky relative to the Earth. At each epoch (instant in time) the receiver gets a signal from each satellite from which it can derive the *pseudorange* to the satellite. In more detail, the GPS receiver gets a signal identifying the satellite along with the time stamp of when the signal was transmitted. The GPS satellite has an atomic clock on board so this time stamp is extremely accurate. The signal travels at the speed of light, which is constant in a vacuum, so in theory the GPS should be able to produce an extremely accurate distance measurement to the measurement by measuring how long the signal took to reach the receiver. There are several problems with that. First, the signal is not traveling through a vacuum, but through the atmosphere. The atmosphere causes the signal to bend, so it is not traveling in a straight line. This causes the signal to take longer to reach the receiver than theory suggests. Second, the on board clock on the GPS *receiver* is not very accurate, so deriving an exact time duration is nontrivial. Third, in many environments the signal can bounce off of buildings, trees, and other objects, causing either a longer path or *multipaths*, in which case the receive receives both the original signal from space and the reflected signals. \n",
     "\n",
     "Let's look at this graphically. I will due this in 2D just to make it easier to graph and see, but of course this will generalize to three dimensions. We know the position of each satellite and the range to each (the range is called the *pseudorange*; we will discuss why later). We cannot measure the range exactly, so there is noise associated with the measurement, which I have depicted with the thickness of the lines. Here is an example of four pseudorange readings from four satellites. I positioned them in a configuration which is unlikely for the actual GPS constellation merely to make the intersections easy to visualize. Also, the amount of error shown is not to scale with the distances, again to make it easier to see."
    ]
@@ -2899,7 +2876,7 @@
        "hBDiM/8/3QDBZRpvieYAAAAASUVORK5CYII=\n"
       ],
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fce1a2647f0>"
+       "<matplotlib.figure.Figure at 0x7f807e43e630>"
       ]
      },
      "metadata": {},
@@ -2920,9 +2897,9 @@
     "\n",
     "However, since GPS is a 3D system we need to solve for the 3 dimensions of space, and 1 dimension of time. That is 4 unknowns, so in theory with 4 satellites we have all the information we need. However, we normally have at least 6 satellites in view, and often more than 6. This means the system is *overdetermined*. Finally, because of the noise in the measurements none of pseudoranges intersect exactly.\n",
     "\n",
-    "If you are well versed in linear algebra you know that this an extremely common problem in scientific computing, and that there are various techniques for solving overdetermied systems. Probably the most common approach used by GPS receivers to find the position is the *iterative least squares* algorithm, commonly abbreviated ILS. As you know, if the errors are Gaussian then the least squares algorithm finds the optimal solution. In other words, we want to minimize the square of the residuals for an overdetermined system.\n",
+    "If you are well versed in linear algebra you know that this an extremely common problem in scientific computing, and that there are various techniques for solving overdetermined systems. Probably the most common approach used by GPS receivers to find the position is the *iterative least squares* algorithm, commonly abbreviated ILS. As you know, if the errors are Gaussian then the least squares algorithm finds the optimal solution. In other words, we want to minimize the square of the residuals for an overdetermined system.\n",
     "\n",
-    "Let's start with some definitions which should be familar to you. First, we define the innovation as \n",
+    "Let's start with some definitions which should be familiar to you. First, we define the innovation as \n",
     "\n",
     "$$\\delta \\mathbf{z}^-= \\mathbf{z} - h(\\mathbf{x}^-)$$\n",
     "\n",
@@ -3018,7 +2995,6 @@
     "We also know that\n",
     "$$\\frac{\\partial \\mathbf{B}^\\mathsf{T}\\mathbf{AB}}{\\partial \\mathbf{B}} = \\mathbf{B}^\\mathsf{T}(\\mathbf{A} + \\mathbf{A}^\\mathsf{T})$$\n",
     "\n",
-    "\n",
     "Therefore the first term becomes\n",
     "\n",
     "$$\n",
@@ -3097,7 +3073,7 @@
     "\n",
     "$$ \\frac{estimated\\_x\\_position - satellite\\_x\\_position}{estimated\\_range\\_to\\_satellite}$$\n",
     "\n",
-    "The equation for $y$ just subtitutes $y$ for $x$.\n",
+    "The equation for $y$ just substitutes $y$ for $x$.\n",
     "\n",
     "Then the algorithm is as follows.\n",
     "\n",
@@ -3344,7 +3320,7 @@
    "source": [
     "Here we can see that the noise means that we no longer find the exact solution but we are still able to quickly converge onto a more accurate solution than the first iteration provides.\n",
     "\n",
-    "This is far from a complete coverage of the iterated least squares algorithm, let alone methods used in GNSS to compute positions from GPS pseudoranges. You will find a number of approaches in the literature, including QR decomposition, SVD, and other techniques to solve the overdetermined system. For a nontrivial task you will have to survey the literature and perhaps design your algorithm depending on your specifc sensor configuration, the amounts of noise, your accuracy requirements, and the amount of computation you can afford to do."
+    "This is far from a complete coverage of the iterated least squares algorithm, let alone methods used in GNSS to compute positions from GPS pseudoranges. You will find a number of approaches in the literature, including QR decomposition, SVD, and other techniques to solve the overdetermined system. For a nontrivial task you will have to survey the literature and perhaps design your algorithm depending on your specific sensor configuration, the amounts of noise, your accuracy requirements, and the amount of computation you can afford to do."
    ]
   },
   {
diff --git a/10_Unscented_Kalman_Filter.ipynb b/10_Unscented_Kalman_Filter.ipynb
index 619f168..ffba5b9 100644
--- a/10_Unscented_Kalman_Filter.ipynb
+++ b/10_Unscented_Kalman_Filter.ipynb
@@ -5540,11 +5540,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This should hold no suprises for you. Now let's implement this filter as an Unscented Kalman filter. Again, this is purely for educational purposes; using a UKF for a linear filter confers no benefit.\n",
+    "This should hold no surprises for you. Now let's implement this filter as an Unscented Kalman filter. Again, this is purely for educational purposes; using a UKF for a linear filter confers no benefit.\n",
     "\n",
     "The equations of the UKF are implemented for you with the `FilterPy` class `UnscentedKalmanFilter`; all you have to do is specify the matrices and the nonlinear functions *f(x)* and *h(x)*. *f(x)* implements the state transition function that is implemented by the matrix $\\mathbf{F}$ in the linear filter, and *h(x)* implements the measurement function implemented with the matrix $\\mathbf{H}$. In nonlinear problems these functions are nonlinear, so we cannot use matrices to specify them.\n",
     "\n",
-    "For our linear problem we can just define these functions to implement the linear equations. The function *f(x)* takes the signature `def f(x,dt)` and *h(x)* takes the signature `def h(x)`. Below is a reasonable implementation of these two functions. Each is expected to return a 1-D numpy array with the result."
+    "For our linear problem we can just define these functions to implement the linear equations. The function *f(x)* takes the signature `def f(x,dt)` and *h(x)* takes the signature `def h(x)`. Below is a reasonable implementation of these two functions. Each is expected to return a 1-D NumPy array with the result."
    ]
   },
   {
@@ -5983,7 +5983,7 @@
    "source": [
     "Let's tackle our first nonlinear problem. To minimize the number of things that change I will keep the problem formulation very similar to the linear tracking problem above. For this problem we will write a filter to track a flying airplane using a stationary radar as the sensor. To keep the problem as close to the previous one as possible we will track in two dimensions, not three. We will track one dimension on the ground and the altitude of the aircraft. The second dimension on the ground adds no difficulty or different information, so we can do this with no loss of generality.\n",
     "\n",
-    " Radars work by emitting a beam of radio waves and scanning for a return bounce. Anything in the beam's path will reflects some of the signal back to the radar. By timing how long it takes for the reflected signal to get back to the radar the system can compute the *slant distance* - the straight line distance from the radar installation to the object. We also get the bearing to the target. For this 2D problem that will be the angle above the ground plane. True integration of sensors for applications like military radar and aircraft navigation systems have to take many factors into account which I am not currently interested in trying to cover in this book. So if any practioners in the the field are reading this they will be rightfully scoffing at my exposition. My only defense is to argue that I am not trying to teach you how to design a military grade radar tracking system, but instead trying to familarize you with the implementation of the UKF. \n",
+    " Radars work by emitting a beam of radio waves and scanning for a return bounce. Anything in the beam's path will reflects some of the signal back to the radar. By timing how long it takes for the reflected signal to get back to the radar the system can compute the *slant distance* - the straight line distance from the radar installation to the object. We also get the bearing to the target. For this 2D problem that will be the angle above the ground plane. True integration of sensors for applications like military radar and aircraft navigation systems have to take many factors into account which I am not currently interested in trying to cover in this book. So if any practitioners in the the field are reading this they will be rightfully scoffing at my exposition. My only defense is to argue that I am not trying to teach you how to design a military grade radar tracking system, but instead trying to familiarize you with the implementation of the UKF. \n",
     " \n",
     "For this example we want to take the slant range measurement from the radar and compute the horizontal position (distance of aircraft from the radar measured over the ground) and altitude of the aircraft, as in the diagram below."
    ]
@@ -6372,7 +6372,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now let's put it all together. A military grade radar system can achieve 1 meter RMS range accuracy, and 1 mrad RMS for bearing [1]. We will assume a more modest 5 meter range accuracy, and 0.5$^\\circ$ anglular accuracy as this provides a more challenging data set for the filter.\n",
+    "Now let's put it all together. A military grade radar system can achieve 1 meter RMS range accuracy, and 1 mrad RMS for bearing [1]. We will assume a more modest 5 meter range accuracy, and 0.5$^\\circ$ angular accuracy as this provides a more challenging data set for the filter.\n",
     "\n",
     "I'll start with the aircraft positioned directly over the radar station, flying away from it at 100 m/s. A typical radar might update only once every 12 seconds and so we will use that for our update period. "
    ]
@@ -9067,9 +9067,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now let's consider a simple example of sensor fusion. Suppose we have some type of doppler system that produces a velocity estimate with 2 m/s RMS accuracy. I say \"some type\" because as with the radar I am not trying to teach you how to create an accurate filter for a doppler system, where you have to account for the signal to noise ratio, atmospheric effects, the geometry of the system, and so on. \n",
+    "Now let's consider a simple example of sensor fusion. Suppose we have some type of Doppler system that produces a velocity estimate with 2 m/s RMS accuracy. I say \"some type\" because as with the radar I am not trying to teach you how to create an accurate filter for a Doppler system, where you have to account for the signal to noise ratio, atmospheric effects, the geometry of the system, and so on. \n",
     "\n",
-    "The accuracy of the radar system in the last examples allowed us to estimate velocities to withn a m/s or so, so we have to degrade that accuracy to be able to easily see the effect of the sensor fusion. Let's change the range error to 500 meters and then compute the standard deviation of the computed velocity. I'll skip the first several measurements because the filter is converging during that time, causing artificially large deviations."
+    "The accuracy of the radar system in the last examples allowed us to estimate velocities to within a m/s or so, so we have to degrade that accuracy to be able to easily see the effect of the sensor fusion. Let's change the range error to 500 meters and then compute the standard deviation of the computed velocity. I'll skip the first several measurements because the filter is converging during that time, causing artificially large deviations."
    ]
   },
   {
@@ -10478,7 +10478,7 @@
    "source": [
     "By incorporating the velocity sensor we were able to reduce the standard deviation from 3.5 m/s to 1.3 m/s. \n",
     "\n",
-    "Sensor fusion is a large topic, and this is a rather simplistic implementation. In a typical navigation problem we have sensors that provide *complementary* information. For example, a GPS might provide somewhat accurate position updates once a second with poor velocity estimation while an inertial system might provide very accurate velocity updates at 50Hz but terrible position estimates. The strenghs and weaknesses of each sensor are orthoganol to each other. This leads to something called the *Complementary filter*, which uses the high update rate of the inertial sensor with the position accurate but slow estimates of the GPS to produce very high rate yet very accurate position and velocity estimates. This will be the topic of a future chapter. "
+    "Sensor fusion is a large topic, and this is a rather simplistic implementation. In a typical navigation problem we have sensors that provide *complementary* information. For example, a GPS might provide somewhat accurate position updates once a second with poor velocity estimation while an inertial system might provide very accurate velocity updates at 50Hz but terrible position estimates. The strengths and weaknesses of each sensor are orthogonal to each other. This leads to something called the *Complementary filter*, which uses the high update rate of the inertial sensor with the position accurate but slow estimates of the GPS to produce very high rate yet very accurate position and velocity estimates. This will be the topic of a future chapter. "
    ]
   },
   {
@@ -12030,7 +12030,7 @@
    "source": [
     "This is untenable behavior for a real world filter. `FilterPy`'s UKF code allows you to provide it a function to compute the residuals in cases of nonlinear behavior like this, but this requires more knowledge about `FilterPy`'s implementation than we yet process.\n",
     "\n",
-    "Finally, let's discuss the sensfor fusion method that we used. We used the measurement from each bearing and had the Kalman filter's equations compute the world position from the measurements. This is equivalent to doing a weighted least squares solution. In the *Kalman Filter Math* chapter we discussed the limited accuracy of such a scheme and introduced an *Iterative Least Squares* (ILS) algorithm to produce greater accuracy. If you wanted to use that scheme you would write an ILS algorithm to solve the geometry of your problem, and pass the result of that calculation into the `update()` method as the measurement to be filtered. This imposes on you the need to compute the correct noise matrix for this computed positions, which may not be trivial. Perhaps in a later release of this book I will develop an example, but regardless it will take you a significant amount of research and experiment to design the best method for your application. For example, the ILS is probably the most common algorithm used to turn GPS pseudoranges into a position, but the literature discusses a number of alternatives. Sometimes there are faster methods, sometimes the iterative method does not converge, or does not converge fast enough, or requires too much computation for an embedded system. "
+    "Finally, let's discuss the sensor fusion method that we used. We used the measurement from each bearing and had the Kalman filter's equations compute the world position from the measurements. This is equivalent to doing a weighted least squares solution. In the *Kalman Filter Math* chapter we discussed the limited accuracy of such a scheme and introduced an *Iterative Least Squares* (ILS) algorithm to produce greater accuracy. If you wanted to use that scheme you would write an ILS algorithm to solve the geometry of your problem, and pass the result of that calculation into the `update()` method as the measurement to be filtered. This imposes on you the need to compute the correct noise matrix for this computed positions, which may not be trivial. Perhaps in a later release of this book I will develop an example, but regardless it will take you a significant amount of research and experiment to design the best method for your application. For example, the ILS is probably the most common algorithm used to turn GPS pseudoranges into a position, but the literature discusses a number of alternatives. Sometimes there are faster methods, sometimes the iterative method does not converge, or does not converge fast enough, or requires too much computation for an embedded system. "
    ]
   },
   {
@@ -19770,7 +19770,7 @@
     "\n",
     "However, if we have measurements from the future we can often figure out if a turn was made or not. Suppose the subsequent measurements all continue turning left. We can then be sure that the measurement was not very noisy, but instead a true indication that a turn was initiated. On the other hand, if the subsequent measurements continued on in a straight line we would know that the measurement was noisy and should be mostly ignored.\n",
     "\n",
-    "We will not develop the math or algorithm here, I will just show you how to call the algorithm in `filterpy`. The algorithm that we have implemented is called an *RTS smoother*, after the three inventors of the algorithm: Rauch, Tung, and Striebel.\n",
+    "We will not develop the math or algorithm here, I will just show you how to call the algorithm in `FilterPy`. The algorithm that we have implemented is called an *RTS smoother*, after the three inventors of the algorithm: Rauch, Tung, and Striebel.\n",
     "\n",
     "The routine is `UnscentedKalmanFilter.rts_smoother()`. Using it is trivial; we pass in the means and covariances computed from the `batch_filter` step, and receive back the smoothed means, covariances, and Kalman gain."
    ]
@@ -21487,7 +21487,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "I have found the literature on choosing values for $\\kappa$ to be rather lacking. Julier's original paper suggests a value of $n+\\kappa=3$ if the distribution is Gaussian, and if not a \"different choice of $\\kappa$ might be more appropriate\"[2]. He also describes it as a \"fine tun[ing\" parameter. So let's just explore what it does. I will let $n=1$ just to minimize the size of the arrays we need to look at."
+    "I have found the literature on choosing values for $\\kappa$ to be rather lacking. Julier's original paper suggests a value of $n+\\kappa=3$ if the distribution is Gaussian, and if not a \"different choice of $\\kappa$ might be more appropriate\"[2]. He also describes it as a \"fine tuning\" parameter. So let's just explore what it does. I will let $n=1$ just to minimize the size of the arrays we need to look at."
    ]
   },
   {