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Probabilistic Models in Supervised Learning by DUDEKULA VIDYASAGAR is a document available to read on EtoBox.
What is Probabilistic Models in Supervised Learning about?
The document discusses probabilistic models for supervised learning. It recaps that a probabilistic model has two key components: an observation model (likelihood) and a prior distribution over unknown parameters. These specify the joint distribution of data and parameters. Point estimation of parameters can be done via maximum likelihood estimation (MLE) or maximum a posteriori (MAP) estimation. MLE maximizes the likelihood while MAP maximizes the posterior by incorporating a prior distribution as regulari
- Author
- DUDEKULA VIDYASAGAR
- Language
- EN