daviddoji/Kalman-and-Bayesian-Filters-in-Python
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Defined categorical distribution.

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Roger Labbe 2015-08-23 18:04:30 -07:00
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02-Discrete-Bayes.ipynb
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@ -392,7 +392,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"We call this a <i>multimodal</i> distribution because we have multiple beliefs about the position of our dog. Of course we are not saying that we think he is simultaneously in three different locations, merely that so far we have narrowed down our knowledge in his position to be one of these three locations. My (Bayesian) belief is that there is a 33.3% chance of being at door 0, 33.3% at door 1, and a 33.3% chance of being at door 8. \n",
"This distribution is called a **categorical distribution**, which is the term used to describe a discrete distribution describing the probability of observing $n$ outcomes. It is a **multimodal distribution** because we have multiple beliefs about the position of our dog. Of course we are not saying that we think he is simultaneously in three different locations, merely that so far we have narrowed down our knowledge in his position to be one of these three locations. My (Bayesian) belief is that there is a 33.3% chance of being at door 0, 33.3% at door 1, and a 33.3% chance of being at door 8. More specifically, \n",
"\n",
"I hand coded the `belief` array in the code above. How would we implement this in code? Well, hallway represents each door as a 1, and wall as 0, so we will multiply the hallway variable by the percentage, like so;"
]