Computational statistics
The bootstrap, cross-validation, PCA and Markov chain Monte Carlo.
13 planned
No page in this subject is open yet. The planned list below shows what is coming, in learning order.
Planned
In learning order. These are not pages yet.
- Kernel density estimation and the bandwidthAdvanced undergrad
- Rejection sampling: drawing from a density you can only evaluateAdvanced undergrad
- Principal component analysis: the directions of most varianceAdvanced undergrad
- Importance sampling: sampling where the answer livesGraduate
- The bootstrap: resampling your own dataAdvanced undergrad
- Cross-validation and overfittingAdvanced undergrad
- When the bootstrap failsGraduate
- The bias-variance tradeoffAdvanced undergrad
- Permutation tests: shuffling the labelsAdvanced undergrad
- The EM algorithm for mixture modelsGraduate
- Markov chain Monte Carlo: the Metropolis algorithmGraduate
- MCMC diagnostics: burn-in, mixing, and effective sample sizeGraduate
- Gibbs samplingGraduate
How it connects
Builds on Statistical inference Probability Linear algebra Numerical methods Regression Stochastic processes Bayesian statistics Time series
Leads to Bonds and interest rates Market risk
Subjects marked “connection” are linked by a related idea, not by a prerequisite.