Minor rewording. function->method, mostly.

05-Multivariate-Gaussians.ipynb

@@ -360,7 +360,7 @@
     "$$f(\\mathbf{x},\\, \\mu,\\,\\Sigma) = \\frac{1}{(2\\pi)^{\\frac{n}{2}}|\\Sigma|^{\\frac{1}{2}}}\\, \\exp  \\Big [{ -\\frac{1}{2}(\\mathbf{x}-\\mu)^\\mathsf{T}\\Sigma^{-1}(\\mathbf{x}-\\mu) \\Big ]}\n",
     "$$\n",
     "\n",
-    "I urge you to not try to remember this function. We will program it in a Python function and then call it if we need to compute a specific value. Plus, the Kalman filter equations compute this for us automatically; we never have to explicitly compute it. However, note that it has the same form as the univariate normal distribution. It uses matrices instead of scalar values, and the root of $\\pi$ is scaled by $n$. If you set n=1 then it turns into the univarate equation. Here is the univariate equation for reference:\n",
+    "I urge you to not try to remember this equation. We will program it in a Python function and then call it if we need to compute a specific value. Plus, the Kalman filter equations compute this for us automatically; we never have to explicitly compute it. However, note that it has the same form as the univariate normal distribution. It uses matrices instead of scalar values, and the root of $\\pi$ is scaled by $n$. If you set n=1 then it turns into the univarate equation. Here is the univariate equation for reference:\n",
     "\n",
     "$$ \n",
     "f(x, \\mu, \\sigma) = \\frac{1}{\\sigma\\sqrt{2\\pi}} \\exp \\Big [{-\\frac{1}{2}}{(x-\\mu)^2}/\\sigma^2 \\Big ]\n",

06-Multivariate-Kalman-Filters.ipynb

@@ -1343,7 +1343,7 @@
     "zs  = [None] * count\n",
     "cov = [None] * count```\n",
     "\n",
-    "Finally we get to the filter. All we need to do is perform the update and predict steps of the Kalman filter for each measurement. The `KalmanFilter` class provides the two functions `update()` and `predict()` for this purpose. `update()` performs the measurement update step of the Kalman filter, and so it takes a variable containing the sensor measurement. \n",
+    "Finally we get to the filter. All we need to do is perform the update and predict steps of the Kalman filter for each measurement. The `KalmanFilter` class provides the two methods `update()` and `predict()` for this purpose. `update()` performs the measurement update step of the Kalman filter, and so it takes a variable containing the sensor measurement. \n",
     "\n",
     "Absent the bookkeeping work of storing the filter's data, the for loop reads:\n",
     "\n",
@@ -1357,7 +1357,7 @@
     "\n",
     "Now let's look at the result. Here is some code that calls `filter_track()` and then plots the result. It is fairly uninteresting code, so I will not walk through it. The `DogSimulation` class from the previous chapter has been placed in `DogSimulation.py`. I have also implemented a few plot routines to visualize the data in  `mkf_internal.py`. Both of these are in the *code* subdirectory if you wish to read them.\n",
     "\n",
-    "The Kalman filter is designed as a recursive algorithm - as new measurements come in we immediately create a new estimate. But it is very common to have a set of data that have been already collected which we want to filter. Kalman filters can always be run in a *batch* mode, where all of the measurements are filtered at once. We have implemented this in `KalmanFilter.batch_filter()`. Internally, all the function does is loop over the measurements and collect the resulting state and covariance estimates in arrays.\n",
+    "The Kalman filter is designed as a recursive algorithm - as new measurements come in we immediately create a new estimate. But it is very common to have a set of data that have been already collected which we want to filter. Kalman filters can always be run in a *batch* mode, where all of the measurements are filtered at once. We have implemented this in `KalmanFilter.batch_filter()`. Internally, all the method does is loop over the measurements and collect the resulting state and covariance estimates in arrays.\n",
     "\n",
     "Here is an alternative form of the last function which uses batch filtering. `batch_filter` returns four NumPy `Array` objects; the first two contain the filtered estimates and covariances (the posteriors), and the last two contain the predicted estimates and covariances (the priors). The priors are useful for smoothing algorithms which we will learn later, but for now they are not useful to us, so I disregard them."
    ]