Getting Started
Data Handling
Desing Policy
Visualization
Model Fitting
Fitting a model’s parameters with run-at-a-time optimization
Step-at-a-time optimization
Parallel Model Fitting
Fitting a model with Markov Chain Monte Carlo
Estimating penny population parameters
Geographic Analyses
Model Comparison
Sensitivity
Surrogating Functions
Testing
Realtime Data Incorporation
Model Development Workflow
Wrapper EMAWorkbench
Data Used in this Cookbook
Chapters to be Written
End Notes
PySD-Cookbook
Model Fitting
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Model Fitting
Fitting a model’s parameters with run-at-a-time optimization
About this technique
Ingredients:
Recipe
Step-at-a-time optimization
About this technique
Ingredients
The Recipe
Parallel Model Fitting
When to use this technique
Ingredients
The Recipe
Fitting a model with Markov Chain Monte Carlo
Ingredients
Recipe
Resources:
Estimating penny population parameters
Load Model
Load Data
Set up models
Set up a Markov Chain Monte Carlo Analysis
Perform the MCMC Sampling
Plot the results