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 (james.p.houghton@gmail.com) or post on the Github issue tracker.
This is a notional listing of desired chapters, with existing chapters
Model Fitting to data
optimizing in the phase space
Frequency Domain
Fitting to multiple datasets
Fitting with unobserved states/ unobserved stocks
Monte Carlo Analysis
Sensitivity Analysis
Propagation of Uncertainties
Markov Chain Monte Carlo
Estimating parameter distributions for a single model
Reversible jump MCMC for model selection
Working with geographic data
Patch Models
Linking models together
Surrogating model functions with data
Nearest neighbors methods
Plotting and displaying simulation output
Static Visualization
Interactive Plots
Driving a model with external data
Loading and processing data in real time
Kalman filtering on realtime data
Multiple Models
Clustering model outputs
based upon timeseries features
based upon derivative features
based upon phase-plane features
based upon estimated(fit) parameters
Bootstrapping/cross validations
Multiple datasets
Partitioned timeseriese
Statistical screening
Screening for sensitivity analysis
Hidden markov models and dynamic models
Machine Learning predictors and dynamic models
Testing policy robustness
Extreme conditions testing - unit testing on models.
Pysd and Exploratory Modeling Analysis Workbench