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Added link to book in nbviewer to top of each page.

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Roger Labbe 2014-05-16 14:16:51 -07:00
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commit 0b37a768de
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14
Gaussians.ipynb
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"#Gaussian Probabilities\n",
"\n",
"Before we begin, we need to set the book's style."
"<center>[Kalman Filters and Random Signals in Python](http://github.com/rlabbe/Kalman-Filters-and-Random-Signals-in-Python)</center>\n"
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"#Gaussian Probabilities\n",
"\n",
"### Introduction\n",
"\n",
"The last chapter ended by discussing some of the drawbacks of the Discrete Bayesian filter. For many tracking and filtering problems our desire is to have a filter that is *unimodal* and *continuous*. That is, we want to model our system using floating point math (continuous) and to have only one belief represented (unimodal). For example, we want to say an aircraft is at (12.34381, -95.54321,2389.5) where that is latitude, longitude, and altidue. We do not want our filter to tell us \"it might be at (1,65,78) or at (34,656,98)\" That doesn't match our physical intuition of how the world works, and as we discussed, it is prohibitively expensive to compute.\n",

6
Introduction.ipynb
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"Before we begin, we need to set the book's style."
"<center>[Kalman Filters and Random Signals in Python](http://github.com/rlabbe/Kalman-Filters-and-Random-Signals-in-Python)</center>"
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"#Introduction\n",
"##### Version 0.1\n",
"##### Version 0.0\n",
"\n",
"testthis\n",
"\n",

19
Kalman_Filters.ipynb
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"# Kalman Filters\n",
"Now that we understand the histogram filter and Gaussians we are prepared to implement a 1D Kalman filter. We will do this exactly as we did the histogram filter - rather than going into the theory we will just develop the code step by step. But first, let's set the book style."
"<center>[Kalman Filters and Random Signals in Python](http://github.com/rlabbe/Kalman-Filters-and-Random-Signals-in-Python)</center>"
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"# Kalman Filters\n",
"Now that we understand the histogram filter and Gaussians we are prepared to implement a 1D Kalman filter. We will do this exactly as we did the histogram filter - rather than going into the theory we will just develop the code step by step. But first, let's set the book style."
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232
Multidimensional_Kalman_Filters.ipynb
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"<h1 align=\"center\">Multidimensional</h1>\n",
"<h1 align=\"center\">Kalman Filters</h1>\n",
"Before we begin, we need to set the book's style."
"<center>[Kalman Filters and Random Signals in Python](http://github.com/rlabbe/Kalman-Filters-and-Random-Signals-in-Python)</center>"
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g-h_filter.ipynb
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"### Building Intuition via Thought Experiments\n",
"\n",
"Before we begin, we need to set the book's style."
"<center>[Kalman Filters and Random Signals in Python](http://github.com/rlabbe/Kalman-Filters-and-Random-Signals-in-Python)</center>"
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"### Building Intuition via Thought Experiments\n",
".\n",
"Imagine we live in a world without scales - the devices you stand on to weigh yourself. One day at work a coworker comes running up to you and announces her invention to you. After she explains, you eagerly stand on it and announce the results: \"172 lbs\". You are estatic - for the first time in your life you know what you weigh. More importantly, dollar signs dance in your eyes as you imagine selling this device to weight loss clinics across the world! This is fantastic!\n",
"\n",
"Another coworker hears the commotion and walks over to find out what has you so excited. You explain the invention and once again step onto the scale, and proudly proclaim the result: \"161 lbs.\" And then you hesitate, confused.\n",

4
histogram_filter.ipynb
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