Remix and Share
Essentials of Probability and Statistical Inference IV: Algorithmic and Nonparametric Approaches
(Complete Item Description)
- Abstract:
Introduces the theory and application of modern, computationally-based methods for exploring and drawing inferences from data. Covers re-sampling methods, non-parametric regression, prediction, and dimension reduction and clustering. Specific topics include Monte Carlo simulation, bootstrap cross-validation, splines, local weighted regression, CART, random forests, neural networks, support vector machines, and hierarchical clustering. De-emphasizes proofs and replaces them with extended discussion of interpretation of results and simulation and data analysis for illustration.
- Subject:
- Mathematics and Statistics, Science and Technology, Social Sciences
- Grade Level:
- Post-secondary
- Collection:
- JHSPH OpenCourseWare
