Ecological Modeling

  1. Advanced Modeling Techniques
    1. Statistical and Machine Learning Approaches
      1. Generalized Linear Models
        1. Error Distributions
          1. Model Selection
          2. Generalized Additive Models
            1. Smooth Functions
              1. Nonlinear Relationships
                1. Interaction Terms
                2. Mixed-Effects Models
                  1. Random Effects
                    1. Hierarchical Structure
                      1. Repeated Measures
                      2. Species Distribution Models
                        1. Environmental Predictors
                          1. Presence-Only Data
                            1. Presence-Absence Data
                              1. Model Evaluation Metrics
                                1. Ensemble Modeling
                                2. Machine Learning Algorithms
                                  1. Decision Trees
                                    1. Random Forests
                                      1. Support Vector Machines
                                        1. Neural Networks
                                          1. Deep Learning
                                            1. Model Training
                                              1. Cross-Validation
                                                1. Hyperparameter Tuning
                                              2. Bayesian Modeling
                                                1. Bayesian Inference Principles
                                                  1. Prior Distribution Selection
                                                    1. Posterior Distribution Computation
                                                      1. Hierarchical Bayesian Models
                                                        1. Multilevel Structure
                                                          1. Hyperparameters
                                                            1. Shrinkage Effects
                                                            2. State-Space Models
                                                              1. Process Models
                                                                1. Observation Models
                                                                  1. Hidden States
                                                                    1. Kalman Filtering
                                                                    2. Bayesian Model Selection
                                                                      1. Deviance Information Criterion
                                                                        1. Watanabe-Akaike Information Criterion
                                                                      2. Data Integration and Assimilation
                                                                        1. Data Fusion Techniques
                                                                          1. Sequential Data Assimilation
                                                                            1. Ensemble Kalman Filtering
                                                                              1. Particle Filtering
                                                                                1. Inverse Modeling
                                                                                  1. Parameter Estimation
                                                                                    1. Model Calibration
                                                                                      1. Optimization Algorithms

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                                                                                  7. Spatially Explicit Models

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                                                                                  9. Model Evaluation and Selection