Essays about: "Bayesian network structure learning"
Showing result 1 - 5 of 8 essays containing the words Bayesian network structure learning.
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1. Bayesian Networks for Modelling the Respiratory System and Predicting Hospitalizations
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Bayesian networks can be used to model the respiratory system. Their structure indicate how risk factors, symptoms, and diseases are related and the Conditional Probability Tables enable predictions about a patient’s need for hospitalization. READ MORE
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2. Causal Inference on Tactical Simulations using Bayesian Structure Learning
University essay from Linköpings universitet/Tillämpad matematik; Linköpings universitet/Tekniska fakultetenAbstract : This thesis explores the possibility of using Bayesian Structure Learning and Do-Calculus to perform causal inference on data from tactical combat simulations provided by Saab. A four-step approach is considered whose first step is to find a Bayesian Network from the data using Bayesian Structure Learning and Probability Distribution Fitting. READ MORE
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3. Unsupervised learning of data representations in brain-like neural networks
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Recently, there has been a growing interest in brain-plausible neural networks that closely resemble the brain’s structure. However, conventional networks do not make good models for the brain since these connections are modelled differently, hence the interest in brain-plausible networks. READ MORE
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4. Sum-Product Network in the context of missing data
University essay from KTH/Matematisk statistikAbstract : In recent years, the interest in new Deep Learning methods has increased considerably due to their robustness and applications in many fields. However, the lack of interpretability of these models and the lack of theoretical knowledge about them raises many issues. It is in this context that sum product network models have emerged. READ MORE
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5. Rating corrumption within insurance companies using Bayesian network classifiers
University essay from Umeå universitet/StatistikAbstract : Bayesian Network (BN) classifiers are a type of probabilistic models. The learning process consists of two steps, structure learning and parameter learning. Four BN classifiers will be learned. These are two different Naive Bayes classifiers (NB), one Tree Augmented Naive Bayes classifier (TAN) and one Forest Naive Bayes classifier (FAN). READ MORE
