# Methodology

## Developing the Decision Tree

Our decision tree was developed through a systematic review of:

1. Existing uncertainty quantification methods
2. Practical deployment considerations
3. Case studies from industry and research
4. Feedback from practitioners

## Key Decision Factors

The decision tree considers the following factors:

### 1. Project Constraints

- **Computational budget**: Available resources for training and inference
- **Latency requirements**: Real-time vs. batch processing needs
- **Model architecture**: Compatibility with existing models

### 2. Data Characteristics

- **Dataset size**: Amount of available training data
- **Data quality**: Presence of noise, outliers, or labeling errors
- **Distribution shift**: Expected changes in data distribution

### 3. Uncertainty Requirements

- **Type of uncertainty**: Epistemic, aleatoric, or both
- **Calibration needs**: Requirements for well-calibrated predictions
- **Interpretability**: Need for explainable uncertainty estimates

## Evaluation Criteria

We evaluate UQ methods based on:

- **Accuracy**: Predictive performance
- **Calibration**: Reliability of uncertainty estimates
- **Computational efficiency**: Training and inference costs
- **Ease of implementation**: Integration complexity
- **Robustness**: Performance under distribution shift

## Method Categories

The decision tree guides users toward one of the following method categories:

1. **Single deterministic models**: Baseline approach
2. **Ensemble methods**: Multiple models for uncertainty estimation
3. **Bayesian approaches**: Probabilistic treatment of model parameters
4. **Post-hoc calibration**: Adjusting predictions after training
5. **Test-time augmentation**: Using data augmentation for uncertainty