Hands-on activities associated with the Ecological Forecasting book and graduate class
Book: Dietze, M. 2017. Ecological Forecasting. Princeton University Press https://ecoforecast.org/book
List of activities by Chapter:
Chapter 1: Introduction
- Exercise 01 - R primer
Chapter 2: From Models to Forecasts
- Exercise 02 - From models to forecasts
Chapter 3: Data, Large and Small
- Exercise 03 - Tools for working with data
Chapter 4: Scientific Workflows and the Informatics of Model-Data Fusion
- Exercise 04 - Pair Coding and Github
Chapter 5: Introduction to Bayes
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Exercise 05 - JAGS primer
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Exercise 05B - Bayesian Regression
Chapter 6:Characterizing Uncertainty
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Chapter 06 - Fitting Uncertainties
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Chapter 06 - Hierarchical Bayes
Chapter 8: Latent Variables and State-Space Models
- Exercise 06 - State Space models
Chapter 9: Fusing Data Sources
- Exercise 07 - Fusing time-series data
Chapter 11: Propagating, Analyzing, and Reducing Uncertainty
- Chapter 11 - Uncertainty Propagation and Analysis
Chapter 13: Data Assimilation 1: Analytical Methods
- Exercise 09 - Kalman Filter
Chapter 14: Data Assimilation 2: Monte Carlo Methods
- Exercise 10 - Particle Filter
Chapter 16: Assessing Model Performance
- Exercise 11 - Model Assessment
Chapter 17: Projection and Decision Support
- Exercise 12 - Decision Support
In addition this repository contains the following folders:
- data - Data files used in the exercises
- images - Image files embedded in the exercises
- tutorial - Additional tutorials contributed by previous students
For a list of Git and Github tutorials see http://gist.github.com/Pakillo/63c15c700c9c76fe8032