Refining Nonparametric Mixture Models with Explainability for Smart Building Applications
Bibliographic record
Abstract
Nonparametric mixture models are a powerful and flexible approach to data clustering; they account for uncertainty by using Bayesian inference to obtain a posterior distribution over the model's parameters and a better fit to the data. Additionally, nonparametric mixture models can adaptively adjust the number of components to fit the data. Incorporating asymmetric generalized Gaussian distribution (AGGD) within the mixture framework extends the capabilities of the widely used Gaussian mixture model (GMM) by adding parameters that control the shape and the skewness of the per-component distribution, which enables the model to capture diverse and complex data patterns. Furthermore, we incorporate explainability within our proposed infinite asymmetric generalized Gaussian mixture model (IAGGMM) to provide interpretable insights into the clustering results, enhancing the model's practicality and transparency. This integration facilitates a deeper understanding of the underlying data structures and the rationale behind the model's decisions, fostering trust and promoting the adoption of our approach in various real-world scenarios. In this study, we explore the application of occupancy estimation for optimizing energy efficiency and facility management in smart buildings. Our approach demonstrates superior performance in modelling complex and asymmetric data distributions, resulting in improved accuracy and adaptability for occupancy level estimation. Therefore, we achieve the optimal trade-off between model complexity and accuracy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".