Chapters to be Written

This cookbook is a work in progress. In the future, I’d like to develop examples in the following major areas. If you’d like to suggest additional topics, or have a model and/or dataset that would make for a good case study, feel free to shoot me an email ( or post on the Github issue tracker.

This is a notional listing of desired chapters, with existing chapters

  1. Model Fitting to data

    1. optimizing in the phase space

    2. Frequency Domain

    3. Fitting to multiple datasets

    4. Fitting with unobserved states/ unobserved stocks

  2. Monte Carlo Analysis

    1. Sensitivity Analysis

    2. Propagation of Uncertainties

  3. Markov Chain Monte Carlo

    1. Estimating parameter distributions for a single model

    2. Reversible jump MCMC for model selection

  4. Working with geographic data

  5. Patch Models

  6. Linking models together

  7. Surrogating model functions with data

    1. Nearest neighbors methods

  8. Plotting and displaying simulation output

    1. Static Visualization

    2. Interactive Plots

  9. Driving a model with external data

  10. Loading and processing data in real time

  11. Kalman filtering on realtime data

  12. Multiple Models

  13. Clustering model outputs

  1. based upon timeseries features

  2. based upon derivative features

  3. based upon phase-plane features

  4. based upon estimated(fit) parameters

  1. Bootstrapping/cross validations

  1. Multiple datasets

  2. Partitioned timeseriese

  1. Statistical screening

  1. Screening for sensitivity analysis

  1. Hidden markov models and dynamic models

  2. Machine Learning predictors and dynamic models

  3. Testing policy robustness

  4. Extreme conditions testing - unit testing on models.

  5. Pysd and Exploratory Modeling Analysis Workbench