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This paper gives methodological responses to maritime piracy by way of three models namely, the Ordinal Logistic Regression, Series Hazard Modelling for Maritime Transport Analysis and the Bayesain Networks. The uniqueness of these models in relation to their ability to address some of the pertinent issues associated with modern day piracy is looked at. The study was conducted as a panel analysis based on a longitudinal quasi-experimental research design with focus on piracy attacks that occurred between and culminating finally in the various forecasts based on each of the different models.
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It also helps to have some previous exposure to basic concepts in discrete probability theory independence, conditional independence, and Bayes' rule. You will be able to take a complex task and understand how it can be encoded as a probabilistic graphical model. You will now know how to implement the core probabilistic inference techniques, how to select the right inference method for the task, and how to use inference to reason. You will also know how to take a data set and use it to learn a model, whether from scratch, or to refine or complete a partially specified model.
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Probabilistic graphic models applied to identification of diseases
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Earn a Certificate When you finish every course and complete the hands-on project, you'll earn a Certificate that you can share with prospective employers and your professional network. There are 3 Courses in this Specialization. Course 1. Course 2. Course 3. Daphne Koller Professor School of Engineering. Chevron Right Can I just enroll in a single course?
Probabilistic Graphical Models
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