diff --git a/toc.ipynb b/toc.ipynb index 2fb2279..c863f81 100644 --- a/toc.ipynb +++ b/toc.ipynb @@ -1,7 +1,7 @@ { "metadata": { "name": "", - "signature": "sha256:0aff0f4e48d4c08098fa28ff7a494cb7eb2ce3c107a5a3a665b52c955077fd1c" + "signature": "sha256:62a110cf58205ec15bdd741effe2f73f0740853316b38e5a0a3edbc2d1cd7157" }, "nbformat": 3, "nbformat_minor": 0, @@ -12,7 +12,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "

Kalman Filters and Bayesian Filters in Python

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Kalman and Bayesian Filters in Python

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\n", "Table of Contents\n", @@ -76,8 +76,12 @@ "[**Chapter 10: Numerical Stability**](not implemented)\n", " \n", "EKF and UKF are linear approximations of nonlinear problems. Unless programmed carefully, they are not numerically stable. We discuss some common approaches to this problem.\n", - " \n", - " \n", + "\n", + "[**Chapter ??: Least Squares Filters](not implemented)\n", + "\n", + "Not sure where in order to put this. \n", + "\n", + "\n", "[**Chapter 11: Smoothing**](not implemented)\n", " \n", "Kalman filters are recursive, and thus very suitable for real time filtering. However, they work well for post-processing data. We discuss some common approaches.\n", @@ -108,6 +112,12 @@ "Brief review of statistical math. \n", " \n", "\n", + "\n", + "[**Appendix: Symbols and Notations**](not implemented)\n", + "\n", + "Symbols and notations used in this book. Comparison with notations used in the literature.\n", + "\n", + "\n", "### Github repository\n", "http://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python\n" ]