User:Sooshie/Books/Statistical Learning
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Statistical Learning
[edit]- Statistics
- Exploratory data analysis
- Probability_distribution
- Variance
- Analysis_of_Variance
- Covariate
- Statistical inference
- Algorithmic inference
- Bayesian inference
- Base rate
- Bias (statistics)
- Gibbs sampling
- Cross-entropy method
- Latent variable
- Maximum a posteriori estimation
- Expectation–maximization algorithm
- Expectation propagation
- Kullback–Leibler divergence
- Generative model
- Significance
- Likelihood_ratio_test
- Maximum_likelihood
- Statistical_significance
- Chi-squared_test
- G-test
- Pearson's_chi-squared_test
- Yates's_correction_for_continuity
- McNemar's_test
- Statistical classification
- Statistical classification
- Probability matching
- Discriminative model
- Linear discriminant analysis
- Multiclass LDA
- Multiple discriminant analysis
- Optimal discriminant analysis
- Fisher kernel
- Discriminant function analysis
- Multilinear subspace learning
- Quadratic classifier
- Variable kernel density estimation
- Category utility
- Evaluation of Classification Models
- Data classification (business intelligence)
- Training set
- Test set
- Synthetic data
- Cross-validation (statistics)
- Loss function
- Hinge loss
- Generalization error
- Type I and type II errors
- Sensitivity and specificity
- Precision and recall
- F1 score
- Confusion matrix
- Matthews correlation coefficient
- Receiver operating characteristic
- Lift (data mining)
- Stability in learning
- Bayesian Learning Methods
- Naive Bayes classifier
- Averaged one-dependence estimators
- Bayesian network
- Bayesian additive regression kernels
- Variational message passing
- Markov Models
- Markov model
- Maximum-entropy Markov model
- Hidden Markov model
- Baum–Welch algorithm
- Forward–backward algorithm
- Hierarchical hidden Markov model
- Markov logic network
- Markov chain Monte Carlo
- Markov random field
- Conditional random field
- Predictive state representation
- Regression analysis
- Outline of regression analysis
- Regression analysis
- Dependent and independent variables
- Linear model
- Linear regression
- Least squares
- Linear least squares (mathematics)
- Local regression
- Additive model
- Antecedent variable
- Autocorrelation
- Backfitting algorithm
- Bayesian linear regression
- Bayesian multivariate linear regression
- Binomial regression
- Canonical analysis
- Censored regression model
- Coefficient of determination
- Comparison of general and generalized linear models
- Compressed sensing
- Conditional change model
- Controlling for a variable
- Cross-sectional regression
- Curve fitting
- Deming regression
- Design matrix
- Difference in differences
- Dummy variable (statistics)
- Errors and residuals in statistics
- Errors-in-variables models
- Explained sum of squares
- Explained variation
- First-hitting-time model
- Fixed effects model
- Fraction of variance unexplained
- Frisch–Waugh–Lovell theorem
- General linear model
- Generalized additive model
- Generalized additive model for location, scale and shape
- Generalized estimating equation
- Generalized least squares
- Generalized linear array model
- Generalized linear mixed model
- Generalized linear model
- Growth curve
- Guess value
- Hat matrix
- Heckman correction
- Heteroscedasticity-consistent standard errors
- Hosmer–Lemeshow test
- Instrumental variable
- Interaction (statistics)
- Isotonic regression
- Iteratively reweighted least squares
- Kitchen sink regression
- Lack-of-fit sum of squares
- Leverage (statistics)
- Limited dependent variable
- Linear probability model
- Mallows's Cp
- Mean and predicted response
- Mixed model
- Moderation (statistics)
- Moving least squares
- Multicollinearity
- Multiple correlation
- Multivariate probit
- Multivariate adaptive regression splines
- Newey–West estimator
- Non-linear least squares
- Nonlinear regression
- Logistic Regression
- Logit
- Multinomial logit
- Logistic regression