daviddoji/Kalman-and-Bayesian-Filters-in-Python
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Added explanation for is distributed by symbol.

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Roger Labbe 2015-05-19 16:53:05 -07:00
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04_Gaussians.ipynb
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@ -967,9 +967,9 @@
"\n",
"> *Important*: I will repeat what I wrote at the top of this section: \"A Gaussian...is completely described with two parameters\"\n",
"\n",
"The standard notation for a normal distribution for a random variable $X$ is $X \\sim\\ \\mathcal{N}(\\mu,\\sigma^2)$. This means I can express the temperature reading of our thermometer as\n",
"The standard notation for a normal distribution for a random variable $X$ is $X \\sim\\ \\mathcal{N}(\\mu,\\sigma^2)$ where $\\sim$ means *distributed according to*. This means I can express the temperature reading of our thermometer as\n",
"\n",
"$$temp = \\mathcal{N}(22,4)$$\n",
"$$temp \\sim \\mathcal{N}(22,4)$$\n",
"\n",
"This is an **extremely important** result. Gaussians allow me to capture an infinite number of possible values with only two numbers! With the values $\\mu=22$ and $\\sigma^2=4$ I can compute the distribution of measurements for over any range."
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