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        "author": "Jakub Pr\u00fcher",
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        "description": "# SSM Toybox\nPython 3 implementation of the nonlinear sigma-point filters based on Bayesian quadrature, such as\n\n* Gaussian Process Quadrature Kalman Filter [1]\n* Student's t-Process Quadrature Kalman Filter [2]\n\nIncluded are also the well-known classical nonlinear Kalman filters such as:\n\n* Extended Kalman Filter\n* Unscented Kalman Filter\n* Cubature Kalman Filter\n* Gauss-Hermite Kalman Filter\n\n\n### Build documentation\n```\ncd docs\nsphinx-apidoc -o ./ ../ssmtoybox ../ssmtoybox/tests\nmake html\n```\n\n\n### Why toybox?\nBecause 'toolbox' sounds too serious :-).\n\n\n### References\n[1]: [[DOI](http://dx.doi.org/10.1109/TAC.2017.2774444) | [PDF](https://arxiv.org/abs/1701.01356)] \nPr\u00c3\u00bcher, J. and Straka, O. Gaussian Process Quadrature Moment Transform, IEEE Transactions on Automatic Control, 2017\n\n[2]: [[DOI](http://dx.doi.org/10.23919/ICIF.2017.8009742) | [PDF](https://arxiv.org/abs/1703.05189)] \nPr\u00c3\u00bcher, J.; Tronarp, F.; Karvonen, T.; S\u00c3\u00a4rkk\u00c3\u00a4, S. and Straka, O. Student-t Process Quadratures for Filtering of \nNon-linear Systems with Heavy-tailed Noise, 20th International Conference on Information Fusion (Fusion), 1-8, 2017 \n\n\n\n",
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        "license": "MIT",
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        "name": "ssmtoybox",
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