Browsing by Subject "Probabilistic models"

Now showing 1 - 2 of 2
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    Exploiting Domain Knowledge as Causal Independencies in Modeling Gestational Diabetes
    (World Scientific, 2023) Mathur, Saurabh; Karanam, Athresh; Radivojac, Predrag; Haas, David M.; Kersting, Kristian; Natarajan, Sriraam; Obstetrics and Gynecology, School of Medicine
    We consider the problem of modeling gestational diabetes in a clinical study and develop a domain expert-guided probabilistic model that is both interpretable and explainable. Specifically, we construct a probabilistic model based on causal independence (Noisy-Or) from a carefully chosen set of features. We validate the efficacy of the model on the clinical study and demonstrate the importance of the features and the causal independence model.
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    A novel probabilistic regression model for electrical peak demand estimate of commercial and manufacturing buildings
    (Elsevier, 2022-02) Taheri, Saman; Razban, Ali; Mechanical Engineering, School of Engineering and Technology
    Due to the high cost of electricity in commercial and industrial sectors, demand forecast models have gained increasing attention. However, there are two unresolved issues: (1) Models are not adaptable when exposed to previously unknown data (2) The value of regression methods vs. state-of-the-art machine learning models has not been made apparent before. This study’s goal is to develop probabilistic demand estimation models. We propose a probabilistic Bayesian regression framework that can not only estimate future demands with high accuracy but also be updated once new information is available. By applying the proposed algorithm to two real-world case studies (commercial and manufacturing), we show a 40.3% and 30.8% improvement in terms of mean absolute error for the two cases. Moreover, the proposed technique outperforms powerful machine learning approaches, including support vector machine by 10.39%, random forest by 6.17%, and multilayer perceptron by 9.14% in terms of mean absolute percentage error.