Tutorial of the probabilistic methods Bayesian networks and influence diagrams applied to medicine

2018 ◽  
Vol 11 (2) ◽  
pp. 112-124 ◽  
Author(s):  
Beatriz Nistal-Nuño
Keyword(s):  
Bayesian Networks ◽  
Probabilistic Methods ◽  
Influence Diagrams

Advanced Sequence Analysis

2021 ◽  
pp. 307-344
Author(s):  
Magy Seif El-Nasr ◽  
Truong Huy Nguyen Dinh ◽  
Alessandro Canossa ◽  
Anders Drachen
Keyword(s):  
Sequence Analysis ◽  
Bayesian Networks ◽  
Recurrent Neural Networks ◽  
Short Term Memory ◽  
Markov Models ◽  
Dynamic Bayesian Networks ◽  
Probabilistic Methods ◽  
Markov Logic ◽  
Markov Decision ◽  
Long Short Term Memory

This chapter discusses more advanced methods for sequence analysis. These include: probabilistic methods using classical planning, Bayesian Networks (BN), Dynamic Bayesian Networks (DBNs), Hidden Markov Models (HMMs), Markov Logic Networks (MLNs), Markov Decision Process (MDP), and Recurrent Neural Networks (RNNs), specifically concentrating on LSTM (Long Short-Term Memory). These techniques are all great but, at this time, are mostly used in academia and less in the industry. Thus, the chapter takes a more academic approach, showing the work and its application to games when possible. The techniques are important as they cultivate future directions of how you can think about modeling, predicting players’ strategies, actions, and churn. We believe these methods can be leveraged in the future as the field advances and will have an impact in the industry. Please note that this chapter was developed in collaboration with several PhD students at Northeastern University, specifically Nathan Partlan, Madkour Abdelrahman Amr, and Sabbir Ahmad, who contributed greatly to this chapter and the case studies discussed.

scholarly journals Hybrid Bayesian Network Models of Spinal Injury and Slip/Fall Events

2020 ◽  
Vol 10 (14) ◽  
pp. 4834
Author(s):  
Richard Hughes
Keyword(s):  
Bayesian Networks ◽  
Spinal Injury ◽  
Network Models ◽  
Probabilistic Methods ◽  
Deterministic Analysis ◽  
Legal Proceedings ◽  
Standard Of Proof ◽  
Biomechanical Models ◽  
Expert Opinions ◽  
Engineering Mechanics

Background: Biomechanists are often asked to provide expert opinions in legal proceedings, especially personal injury cases. This often involves using deterministic analysis methods, although the expert is expected to opine using a civil standard of “more likely than not” that is inherently probabilistic. Methods: A method is proposed for converting a class of deterministic biomechanical models into hybrid Bayesian networks that produce a probability well suited for addressing the civil standard of proof. The method was developed for spinal injury during lifting. Its generalizability was assessed by applying it to slip and fall events based on the coefficients of friction at the shoe–floor interface. Results: The proposed method is shown to be generalizable beyond lifting by applying it to a slip and fall event. Both the lifting and slip and fall models showed that incorporating evidence of injury could change the probabilities of critical quantities exceeding a threshold from “less likely than not” to “more likely than not.” Conclusions: The present work shows that it is possible to develop Bayesian networks for legal use based on laws of engineering mechanics and probabilistic descriptions of measurement error and human variability.

Using Bayesian Networks for Student Modeling

2008 ◽  
pp. 97-113 ◽  
Author(s):  
Chao-Lin Liu
Keyword(s):  
Bayesian Networks ◽  
Graphical Models ◽  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Influence Diagrams ◽  
Student Modeling ◽  
Learning Needs ◽  
Tutoring Systems ◽  
Test Items ◽  
Educational Applications

This chapter purveys an account of Bayesian networks-related technologies for modeling students in intelligent tutoring systems. Uncertainty exists ubiquitously when we infer students’ internal status, for example, learning needs and emotion, from their external behavior, for example, responses to test items and explorative actions. Bayesian networks offer a mathematically sound mechanism for representing and reasoning about students under uncertainty. This chapter consists of five sections, and commences with a brief overview of intelligent tutoring systems, emphasizing the needs for uncertain reasoning. A succinct survey of Bayesian networks for student modeling is provided in Bayesian Networks, and we go through an example of applying Bayesian networks and mutual information to item selection in computerized adaptive testing in Applications to Student Models. We then touch upon influence diagrams and dynamic Bayesian networks for educational applications in More Graphical Models, and wrap up the chapter with an outlook and discussion for this research direction.

BUILDING DETECTION USING BAYESIAN NETWORKS

2000 ◽  
Vol 14 (06) ◽  
pp. 715-733 ◽  
Author(s):  
A. STASSOPOULOU ◽  
T. CAELLI
Keyword(s):  
Feature Extraction ◽  
Bayesian Networks ◽  
Human Behavior ◽  
Probabilistic Methods ◽  
Washington Dc ◽  
Building Detection ◽  
Expert Performance ◽  
Evidence Combination

This paper further explores the uses of Bayesian Networks for detecting buildings from digital orthophotos. This work differs from current research in building detection in so far as it utilizes the ability of Bayesian Networks to provide probabilistic methods for evidence combination and, via training, to determine how such evidence should be weighted to maximize classification. In this vein, then, we have also utilized expert performance to not only configure the network values but also to adapt the feature extraction pre-processing units to fit human behavior as closely as possible. Results from digital orthophotos of the Washington DC area prove that such an approach is feasible, robust and worth further analysis.

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