daviddoji/Kalman-and-Bayesian-Filters-in-Python
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Vocabulary changes.

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Roger Labbe 2015-07-22 11:56:11 -07:00
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02-Discrete-Bayes.ipynb
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@ -679,9 +679,9 @@
"source": [
"We can see that we correctly shifted all values one position to the right, wrapping from the end of the array back to the beginning.\n",
"\n",
"You will often see this referred to as the state **evolution**, which is short for **time evolution** [7]. This term refers to how the state of the system changes over time. For filters, this time is usually discrete. For our dog tracking, the system state is the position of the dog, so the state evolution is the position after a discrete amount of time has passed. I haven't defined 'state' or 'system' yet, but I hope it is clear in context. The **system** is what we are trying to model or filter, and the state is it's current configuration or value. Except in simulations we rarely know the actual state, so we say our filters produce the **estimated state** of the system. In practice this often gets called the state, so be careful to understand if the reference is to the state of the filter (which is the estimated state), or the state of the system (which is the actual state).\n",
"You will often see this referred to as the state or system **evolution**, which is short for **time evolution** [7]. Another term is **system propogation**. This term refers to how the state of the system changes over time. For filters, this time is usually discrete. For our dog tracking, the system state is the position of the dog, so the state evolution is the position after a discrete amount of time has passed. I haven't defined 'state' or 'system' yet, but I hope it is clear in context. The **system** is what we are trying to model or filter, and the state is it's current configuration or value. Except in simulations we rarely know the actual state, so we say our filters produce the **estimated state** of the system. In practice this often gets called the state, so be careful to understand if the reference is to the state of the filter (which is the estimated state), or the state of the system (which is the actual state).\n",
"\n",
"More terminology - this prediction becomes our new *prior*. Time has moved forward and we made a prediction without benefit of knowing the measurements. "
"More terminology - this prediction becomes our new **prior**. Time has moved forward and we made a prediction without benefit of knowing the measurements. "
]
},
{
@ -1883,7 +1883,7 @@
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