Abstract
The most important problem in machine learning is to estimate and infer the value of unknown variables (e.g., class label) based on the observed evidence (e.g., training samples). Probabilistic models provide a framework that considers learning problems as computing the probability distributions of variables.
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Zhou, ZH. (2021). Probabilistic Graphical Models. In: Machine Learning. Springer, Singapore. https://doi.org/10.1007/978-981-15-1967-3_14
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DOI: https://doi.org/10.1007/978-981-15-1967-3_14
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