Merge branch 'master' of https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python

Conflicts:
	01-g-h-filter.ipynb

08-Designing-Kalman-Filters.ipynb

@@ -407,7 +407,7 @@
     "$$\\mathbf{x} = \n",
     "\\begin{bmatrix}x & \\dot{x} & y & \\dot{y}\\end{bmatrix}^\\mathsf{T}$$\n",
     "\n",
-    "There is nothing special about this organization. I could have used $\\begin{bmatrix}x & y & \\dot{x} &  \\dot{y}\\end{bmatrix}^\\mathsf{T}$ or something less logical. I just need to be consistent in the rest of the matrices. I like keeping positions and locations next to each other because it keeps the covariances between positions and velocities in the same sub block of the covariance matrix. In my formulation `P[1,0]` contains the covariance of of $x$ and $\\dot{x}$. In the alternative formulation that covariance is at `P[2, 0]`. This gets worse as the number of dimension increases (e.g. 3D space, accelerations).\n",
+    "There is nothing special about this organization. I could have used $\\begin{bmatrix}x & y & \\dot{x} &  \\dot{y}\\end{bmatrix}^\\mathsf{T}$ or something less logical. I just need to be consistent in the rest of the matrices. I like keeping positions and velocities next to each other because it keeps the covariances between positions and velocities in the same sub block of the covariance matrix. In my formulation `P[1,0]` contains the covariance of of $x$ and $\\dot{x}$. In the alternative formulation that covariance is at `P[2, 0]`. This gets worse as the number of dimension increases (e.g. 3D space, accelerations).\n",
     "\n",
     "Let's pause and address how you identify the hidden variables. This example is somewhat obvious because we've already worked through the 1D case, but other problems won't be obvious There is no easy answer to this question. The first thing to ask yourself is what is the interpretation of the first and second derivatives of the data from the sensors. We do that because obtaining the first and second derivatives is mathematically trivial if you are reading from the sensors using a fixed time step. The first derivative is just the difference between two successive readings. In our tracking case the first derivative has an obvious physical interpretation: the difference between two successive positions is velocity. \n",
     "\n",

13-Smoothing.ipynb

@@ -415,7 +415,7 @@
     "\n",
     "* Fixed Interval Smoothing\n",
     "\n",
-    "This is a batch processing based filter. This filter waits for all of the data to be collected before making any estimates. For example, you may be a scientist collecting data for an experiment, and don't need to know the result until the experiment is complete. A fixed interval smoother will collect all the data than estimate the state at each measurement using all available previous and future measurements. If it is possible for you to run your Kalman filter in batch mode it is always recommended to use one of these filters a it will provide much better results than the recursive forms of the filter from the previous chapters.\n",
+    "This is a batch processing based filter. This filter waits for all of the data to be collected before making any estimates. For example, you may be a scientist collecting data for an experiment, and don't need to know the result until the experiment is complete. A fixed interval smoother will collect all the data, then estimate the state at each measurement using all available previous and future measurements. If it is possible for you to run your Kalman filter in batch mode it is always recommended to use one of these filters a it will provide much better results than the recursive forms of the filter from the previous chapters.\n",
     "\n",
     "\n",
     "* Fixed Lag Smoothing\n",

README.md

@@ -14,7 +14,7 @@ The website http://nbviewer.org provides an IPython Notebook server that renders
 The Preface contains instructions on how to host the book online, for free, on a cloud server (cloud.sagemath.com) that allows you to run and alter the code insde the Notebooks. It takes 5 minutes to set up. 
 
 
-Issues or Questsions
+Issues or Questions
 ------
 
 If you have comments, you can write an issue at GitHub so that everyone can read it along with my response. Please don't view it as a way to report bugs only. Alternatively I've created a gitter room for more informal discussion. [![Join the chat at https://gitter.im/rlabbe/Kalman-and-Bayesian-Filters-in-Python](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/rlabbe/Kalman-and-Bayesian-Filters-in-Python?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)