UsefulLinks
Biology
Ecology and Conservation
Ecological Modeling
1. Introduction to Ecological Modeling
2. Mathematical and Statistical Foundations
3. A Typology of Ecological Models
4. The Modeling Cycle: From Concept to Application
5. Core Models in Population Ecology
6. Models in Community and Ecosystem Ecology
7. Spatially Explicit Models
8. Advanced Modeling Techniques
9. Model Evaluation and Selection
10. Applications in Conservation and Management
11. Philosophical and Practical Considerations
8.
Advanced Modeling Techniques
8.1.
Statistical and Machine Learning Approaches
8.1.1.
Generalized Linear Models
8.1.1.1.
Link Functions
8.1.1.2.
Error Distributions
8.1.1.3.
Model Selection
8.1.2.
Generalized Additive Models
8.1.2.1.
Smooth Functions
8.1.2.2.
Nonlinear Relationships
8.1.2.3.
Interaction Terms
8.1.3.
Mixed-Effects Models
8.1.3.1.
Random Effects
8.1.3.2.
Hierarchical Structure
8.1.3.3.
Repeated Measures
8.1.4.
Species Distribution Models
8.1.4.1.
Environmental Predictors
8.1.4.2.
Presence-Only Data
8.1.4.3.
Presence-Absence Data
8.1.4.4.
Model Evaluation Metrics
8.1.4.5.
Ensemble Modeling
8.1.5.
Machine Learning Algorithms
8.1.5.1.
Decision Trees
8.1.5.2.
Random Forests
8.1.5.3.
Support Vector Machines
8.1.5.4.
Neural Networks
8.1.5.5.
Deep Learning
8.1.5.6.
Model Training
8.1.5.7.
Cross-Validation
8.1.5.8.
Hyperparameter Tuning
8.2.
Bayesian Modeling
8.2.1.
Bayesian Inference Principles
8.2.2.
Prior Distribution Selection
8.2.3.
Posterior Distribution Computation
8.2.4.
Hierarchical Bayesian Models
8.2.4.1.
Multilevel Structure
8.2.4.2.
Hyperparameters
8.2.4.3.
Shrinkage Effects
8.2.5.
State-Space Models
8.2.5.1.
Process Models
8.2.5.2.
Observation Models
8.2.5.3.
Hidden States
8.2.5.4.
Kalman Filtering
8.2.6.
Bayesian Model Selection
8.2.6.1.
Deviance Information Criterion
8.2.6.2.
Watanabe-Akaike Information Criterion
8.3.
Data Integration and Assimilation
8.3.1.
Data Fusion Techniques
8.3.2.
Sequential Data Assimilation
8.3.3.
Ensemble Kalman Filtering
8.3.4.
Particle Filtering
8.3.5.
Inverse Modeling
8.3.5.1.
Parameter Estimation
8.3.5.2.
Model Calibration
8.3.5.3.
Optimization Algorithms
Previous
7. Spatially Explicit Models
Go to top
Next
9. Model Evaluation and Selection