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Modeling and Reasoning with Bayesian networks by Adnan Darwiche. Pattern Recognition and Machine Learning by Chris Bishop. Machine Learning: a Probabilistic Perspective by Kevin P. Murphy. Information Theory, Inference, and Learning Algorithms by David J. C. Mackay. Available online. Bayesian Reasoning and Machine Learning by David Barber Bayesian approach, Modelling, machine learning, Student Performance, Stochastic Modelling. 1. Introduction 1.1. Statement of Purpose Accurate prediction of student performance is helpful in order to provide a student with the necessary assistance in the learning process. However, in a manual set up, it is

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I'm currently going through "Bayesian Reasoning and Machine Learning" by David Barber and it is an extremely well written and engaging book for learning the fundamentals. So a question to someone who has already done this. What are the next set of books I should go through after I have reasonable proficiency with most of the concepts in Barber? Bayesian Computation With R Solutions Manual Bayesian Computation With R Solutions Bayesian Computation With R Solution Manual Bayesian Computation With Bayesian Reasoning And Machine Learning An Introduction To Bayesian Inference And Decision Collapsed Variational Bayesian Inference For Pcfgs Bayesian Grammar Learning For Inverse

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