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Missing Data Problems in Machine Learning by Benjamin M. Marlin is a nonfiction available to read on EtoBox.

What is Missing Data Problems in Machine Learning about?

1 Introduction......Page 9 1.1 Outline and Contributions......Page 10 1.2.1 Notation for Missing Data......Page 12 1.2.2 Notation and Conventions for Vector and Matrix Calculus......Page 13 2.1 Optimal Prediction and Minimizing Expected Loss......Page 15 2.2 The Bayesian Framework......Page 16 2.2.2 Bayesian Computation......Page 17 2.3.1 MAP Approximation to The Prediction Function......Page 19 2.3.2 MAP Computation......Page 20 2.4.1 Function Approximation as Optimization......Page 21 2.4.2 Function Approximation and Regularization......Page 22 2.5.2 Validation Loss......Page 23 2.5.3 Cross Validation Loss......Page 24 3.1 Categories of Missing Data......Page 25 3.2 The Missing at Random Assumption and Multivariate Data......Page 26 3.3 Impact of Incomplete Data on Inference......Page 28 3.4 Missing Data, Inference, and Model Misspecification......Page 29 4.1 Finite Mixture Models......Page 33 4.1.1 Maximum A Posteriori Estimation......Page 35 4.2 Dirichlet Process Mixture Models......Page 37 4.2.1 Properties of The Dirichlet Process......Page 38 4.2.2 Bayesian Inference and the Conjugate Gibbs Sampler......Page 40 4.2.3 Bayesian Inference and the Collapsed Gibbs Sampler......Pag

Who reads Missing Data Problems in Machine Learning?

Author

Benjamin M. Marlin

Language

EN

Category

nonfiction

Subjects

Computer Science, Cybernetics, Stem