fixed calling of methods 'functions'

08-Designing-Kalman-Filters.ipynb

@@ -2686,7 +2686,7 @@
    "source": [
     "We can see from the standard deviation that the performance is a bit worse than when the PS and wheel were measured in every update, but better than the wheel alone.\n",
     "\n",
-    "The code is fairly straightforward. The `update()` function optionally takes R as an argument, and I chose to do that rather than alter `KalmanFilter.R`, mostly to show that it is possible. Either way is fine. I modified `KalmanFilter.H` on each update depending on whether there are 1 or 2 measurements available. The only other difficulty was storing the wheel and PS measurements in two different arrays because there are a different number of measurements for each. "
+    "The code is fairly straightforward. The `update()` method optionally takes R as an argument, and I chose to do that rather than alter `KalmanFilter.R`, mostly to show that it is possible. Either way is fine. I modified `KalmanFilter.H` on each update depending on whether there are 1 or 2 measurements available. The only other difficulty was storing the wheel and PS measurements in two different arrays because there are a different number of measurements for each. "
    ]
   },
   {

10-Unscented-Kalman-Filter.ipynb

@@ -732,7 +732,7 @@
     "\\mathcal{X} &= \\mathtt{sigma\\_function}(\\bf{\\mu}, \\bf{\\Sigma}) \\\\\n",
     "W^m, W^c &= \\mathtt{weight\\_function}(\\mathtt{n, parameters})\\end{aligned}$$\n",
     "\n",
-    "We pass each sigma point through a process function $f(\\mathcal{X})$ which you define. This projects the sigma points forward in time according to the process model.\n",
+    "We pass each sigma point through a state transition function $f(\\mathcal{X})$ which you define. This projects the sigma points forward in time according to the process model.\n",
     "\n",
     "$$\\boldsymbol{\\mathcal{Y}} = f(\\boldsymbol{\\chi})$$"
    ]