Fixed latex errors.

14-Adaptive-Filtering.ipynb

@@ -1682,8 +1682,7 @@
     "\n",
     "The core idea of using several filters to detect a maneuver is sound, but the estimate is jagged when we abruptly transition between the filters. Choosing one filter over the other flies in the face of this entire book, which uses probability to determine the *likelihood* of measurements and models. We don't choose *either* the measurement or prediction, depending on which is more likely, we choose a *blend* of the two in proportion to their likelihoods. We should do the same here. This approach is called the **Multiple Model Adaptive Estimator**, or MMAE.\n",
     "\n",
-    "In the **Designing Kalman Filters** chapter we learned the **likelihood function** $\n",
-    "\\mathcal{L} = \\frac{1}{\\sqrt{2\\pi S}}\\exp [-\\frac{1}{2}\\mathbf{y}^\\mathsf{T}\\mathbf{S}^{-1}\\mathbf{y}]$, which tells us how likely a filter is to be performing optimally given the inputs. We can use this to compute the probability that each filter is the best fit to the data. If we have N filters, we can compute the probability that filter i is correct in relation to the rest of the filters with\n",
+    "In the **Designing Kalman Filters** chapter we learned the **likelihood function** $\\mathcal{L} = \\frac{1}{\\sqrt{2\\pi S}}\\exp [-\\frac{1}{2}\\mathbf{y}^\\mathsf{T}\\mathbf{S}^{-1}\\mathbf{y}]$, which tells us how likely a filter is to be performing optimally given the inputs. We can use this to compute the probability that each filter is the best fit to the data. If we have N filters, we can compute the probability that filter i is correct in relation to the rest of the filters with\n",
     "\n",
     "$$p_k^i = \\frac{\\mathcal{L}_k^ip_{k-1}^i}{\\sum\\limits_{j=1}^N \\mathcal{L}_k^jp_{k-1}^j}$$\n",
     "\n",
@@ -1840,7 +1839,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.4.3"
+   "version": "3.4.1"
   }
  },
  "nbformat": 4,

Appendix-B-Symbols-and-Notations.ipynb

@@ -362,7 +362,7 @@
     "* x: state vector\n",
     "* u: input vector\n",
     "* Process noise or plant noise\n",
-    "* system matrix: A  (for \\dot(x) = Ax + Bu + Dv)\n",
+    "* system matrix: A  (for $\\dot(x) = Ax + Bu + Dv$)\n",
     "* F: state transition matrix\n",
     "* H : Measurement matrix\n",
     "* y : measurement residual\n",