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{"name":"Pysd-cookbook","tagline":"Simple Recipes for Powerful Analysis of System Dynamics Models","body":"[PySD Cookbook](http://jamesphoughton.github.io/PySD-Cookbook)\r\n=============\r\n## Simple recipes for powerful analysis of system dynamics models\r\n*by James Houghton*\r\n\r\n###Chapters:\r\n\r\n0. Getting Started with Computation in Python and Analysis with PySD\r\n 1. [Installing Python and PySD](http://nbviewer.ipython.org/github/JamesPHoughton/PySD-Cookbook/blob/master/1_2_Installation_and_Setup.ipynb)\r\n 1. [Introduction to PySD Usage](http://nbviewer.ipython.org/github/JamesPHoughton/PySD-Cookbook/blob/master/1_3_Hello_World_Teacup.ipynb)\r\n1. Model Fitting to data\r\n 1. [*Run at a time* optimization](http://nbviewer.ipython.org/github/JamesPHoughton/PySD-Cookbook/blob/master/2_1_Fitting_with_Optimization.ipynb)\r\n 2. [*Step at a time* optimization](http://nbviewer.ipython.org/github/JamesPHoughton/PySD-Cookbook/blob/master/2_2_Step_at_a_time_optimization.ipynb)\r\n 3. optimizing in the phase space\r\n 4. Frequency Domain\r\n 4. Fitting to multiple datasets\r\n 5. Fitting with unobserved states/ unobserved stocks\r\n2. Monte Carlo Analysis\r\n 3. Sensitivity Analysis\r\n 4. Propagation of Uncertainties\r\n2. Markov Chain Monte Carlo\r\n 1. Estimating parameter distributions for a single model\r\n 2. Reversible jump MCMC for model selection\r\n3. Patch Models\r\n 1. Linking models together\r\n4. Surrogating model functions with data\r\n 1. [Machine learning Regression models](http://nbviewer.ipython.org/github/JamesPHoughton/PySD-Cookbook/blob/master/6_1_Surrogating_with_regression.ipynb)\r\n 2. Nearest neighbors methods\r\n5. Driving a model with external data\r\n6. Loading and processing data in real time\r\n7. Kalman filtering on realtime data\r\n5. Multiple Models\r\n10. Clustering model outputs\r\n 1. based upon timeseries features\r\n 2. based upon derivative features\r\n 3. based upon phase-plane features\r\n 4. based upon estimated(fit) parameters\r\n11. Bootstrapping/cross validations\r\n 1. Multiple datasets\r\n 2. Partitioned timeseriese\r\n12. Statistical screening\r\n 13. Screening for sensitivity analysis \r\n13. Hidden markov models and dynamic models\r\n14. Machine Learning predictors and dynamic models\r\n15. Testing policy robustness\r\n16. Extreme conditions testing - unit testing on models.\r\n17. Pysd and Exploratory Modeling Analysis Workbench\r\n\r\nThis cookbook is intended as a resource for system dynamics practitioners working to use big data to \r\nimprove their modeling practice. We strive to make each recipe short, simple to understand, and transferable, \r\nso that the script can be copied and adapted to the desired problem.\r\n\r\nWe start each recipe with a description of the technique, and a discussion of how it is useful to the SD practitioner.\r\n","google":"","note":"Don't delete this file! It's used internally to help with page regeneration."}