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Merge pull request #269 from rummanwaqar/master
Bug fix: Issues in Chap 3 and 4
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03-Gaussians.ipynb

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"\n",
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"If $x$ is continuous we substitute the sum for an integral, like so\n",
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"\n",
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"$$\\mathbb E[X] = \\int_{a}^b\\, f(x) \\,dx$$\n",
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"$$\\mathbb E[X] = \\int_{a}^b\\, xf(x) \\,dx$$\n",
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"\n",
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"where $f(x)$ is the probability distribution function of $x$. We won't be using this equation yet, but we will be using it in the next chapter.\n",
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"\n",
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"This curve is a [*probability density function*](https://en.wikipedia.org/wiki/Probability_density_function) or *pdf* for short. It shows the relative likelihood for the random variable to take on a value. We can tell from the chart student is somewhat more likely to have a height near 1.8 m than 1.7 m, and far more likely to have a height of 1.9 m vs 1.4 m. Put another way, many students will have a height near 1.8 m, and very few students will have a height of 1.4 m or 1.9 meaters. Finally, notice that the curve is centered over the mean of 1.8 m.\n",
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"This curve is a [*probability density function*](https://en.wikipedia.org/wiki/Probability_density_function) or *pdf* for short. It shows the relative likelihood for the random variable to take on a value. We can tell from the chart student is somewhat more likely to have a height near 1.8 m than 1.7 m, and far more likely to have a height of 1.9 m vs 1.4 m. Put another way, many students will have a height near 1.8 m, and very few students will have a height of 1.4 m or 2.2 meters. Finally, notice that the curve is centered over the mean of 1.8 m.\n",
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"\n",
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"> I explain how to plot Gaussians, and much more, in the Notebook *Computing_and_Plotting_PDFs* in the \n",
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"Supporting_Notebooks folder. You can read it online [here](https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/blob/master/Supporting_Notebooks/Computing_and_plotting_PDFs.ipynb) [1].\n",

04-One-Dimensional-Kalman-Filters.ipynb

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"\n",
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"$$\\begin{aligned}\n",
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"\\mathcal N(\\mu, \\sigma^2) &= \\| prior \\cdot likelihood \\|\\\\\n",
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"&=\\mathcal{N}(\\bar\\mu, \\bar\\sigma^2)\\cdot \\mathcal{N}(\\mu_z, \\sigma_z^2) \\\\\n",
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"&= \\| \\mathcal{N}(\\bar\\mu, \\bar\\sigma^2)\\cdot \\mathcal{N}(\\mu_z, \\sigma_z^2) \\|\\\\\n",
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"&= \\mathcal N(\\frac{\\bar\\sigma^2 \\mu_z + \\sigma_z^2 \\bar\\mu}{\\bar\\sigma^2 + \\sigma_z^2},\\frac{\\bar\\sigma^2\\sigma_z^2}{\\bar\\sigma^2 + \\sigma_z^2})\n",
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"\\end{aligned}$$\n",
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"\n",

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