daviddoji/Kalman-and-Bayesian-Filters-in-Python
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Roger Labbe 2020-04-26 21:29:03 -07:00
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01-g-h-filter.ipynb
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"cell_type": "markdown",
"metadata": {},
"source": [
"I think this is starting to look really good. Because of the poor initial guess of the weight gain being 1 it takes the filter several days to accurately predict the weight, but once it does that it starts to accurately track the weight. We used no methodology for choosing our scaling factors of $\\frac{4}{10}$ and $\\frac{1}{3}$ (actually, they are poor choices for this problem), but otherwise all of the math followed from very reasonable assumptions. Recall that you can change the value of the parameter `time_step` to a larger value and re-run the cell if you want to see the plot drawn step-by-step.\n",
"I think this is starting to look really good. Because of the poor initial guess of the weight gain being -1 it takes the filter several days to accurately predict the weight, but once it does that it starts to accurately track the weight. We used no methodology for choosing our scaling factors of $\\frac{4}{10}$ and $\\frac{1}{3}$ (actually, they are poor choices for this problem), but otherwise all of the math followed from very reasonable assumptions. Recall that you can change the value of the parameter `time_step` to a larger value and re-run the cell if you want to see the plot drawn step-by-step.\n",
"\n",
"One final point before we go on. In the prediction step I wrote the line\n",
"```python\n",