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README.md

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+---
+language: en
+license: mit
+library_name: thrml
+tags:
+- mortality-prediction
+- actuarial-science
+- probabilistic-modeling
+- thermal-computing
+- energy-based-models
+- life-expectancy
+- death-probability
+- uncertainty-quantification
+pipeline_tag: tabular-classification
+---
+
+# Thermal-Enhanced Morbid AI Model v0.1.1
+
+## Model Description
+
+Initial THRML integration with basic probabilistic mortality modeling capabilities. Includes uncertainty quantification and demographic factor interactions.
+
+This model integrates THRML (Thermodynamic HypergRaphical Model Library) with Morbid AI's mortality prediction capabilities, providing probabilistic predictions with uncertainty quantification.
+
+## Model Architecture
+
+**Type**: thermal_energy_based_model
+**Framework**: THRML + JAX
+**Version**: 0.1.1
+
+### Thermal Features
+
+- Probabilistic graphical models for mortality factors
+- Block Gibbs sampling with demographic blocking
+- Energy-based life expectancy prediction
+- Confidence intervals for all predictions
+- Risk factor analysis and contribution scoring
+
+## Performance Metrics
+
+- **baseline_accuracy**: 0.8500
+- **uncertainty_coverage**: 0.9500
+- **demographic_factors**: 4.0000
+- **sampling_efficiency**: 0.9200
+
+## Usage
+
+```python
+from thermal.models.life_expectancy import LifeExpectancyEBM
+from thermal.graph.mortality_graph import MortalityRecord
+
+# Load mortality data
+mortality_data = [...]  # List of MortalityRecord objects
+
+# Initialize thermal model
+model = LifeExpectancyEBM(mortality_data)
+
+# Make prediction with uncertainty quantification
+prediction = model.predict_life_expectancy(
+    age=45,
+    country="USA", 
+    sex=1,  # 1=male, 2=female, 3=both
+    n_samples=1000,
+    confidence_level=0.95
+)
+
+print(f"Life Expectancy: {prediction.mean_life_expectancy:.1f} years")
+print(f"95% CI: {prediction.confidence_interval}")
+print(f"Uncertainty: {prediction.uncertainty:.2f}")
+```
+
+## Model Configuration
+
+### THRML Parameters
+
+**sampling**:
+  - default_samples: 1000
+  - burn_in: 200
+  - thinning: 2
+  - blocking_strategy: demographic
+**model**:
+  - energy_based: True
+  - uncertainty_quantification: True
+  - demographic_interactions: True
+**performance**:
+  - gpu_acceleration: True
+  - jax_backend: True
+  - memory_efficient: True
+
+### Sampling Configuration
+
+- **Block Gibbs Sampling**: Two-color and demographic blocking strategies
+- **Default Samples**: 1000 MCMC samples
+- **Burn-in**: 200 steps
+- **Thinning**: Every 2nd sample
+
+## Training Data
+
+The model is trained on mortality data including:
+- **Countries**: Global mortality statistics from major countries
+- **Age Range**: 0-100+ years
+- **Time Period**: 2010-2025
+- **Demographic Factors**: Age, sex, country, year
+
+## Limitations
+
+- Model performance depends on availability of demographic-specific training data
+- Uncertainty estimates are calibrated on historical data and may not capture unprecedented events
+- Requires THRML and JAX dependencies for optimal performance
+
+## Version History
+
+### v0.1.1 - 2025-10-29
+
+- Initial THRML integration framework
+- MortalityGraphBuilder for demographic interactions
+- LifeExpectancyEBM with uncertainty quantification
+- Block Gibbs sampling implementation
+- Basic API integration structure
+
+## Citation
+
+```
+@software{thermal_morbid_ai_0_1_1,
+  title={Thermal-Enhanced Morbid AI Model},
+  version={0.1.1},
+  year={2025},
+  url={https://huggingface.co/MorbidCorp/thermal-mortality-model}
+}
+```
+
+## License
+
+MIT License - see LICENSE file for details.
+
+## Contact
+
+For questions about this model, please open an issue in the [Morbid AI repository](https://github.com/AlphaTONCapital/morbid-ai).