(PRML) Ch12.Continuous Latent Variables
PCA, PPCA, Bayesian PCA, FA, Kernel PCA
PRML
(PRML) Ch11.Sampling Methods
Rejection Sampling, Importance Sampling, Slice sampling, Hybrid Monte Carlo
(PRML) Ch10.Approximate Inference
Approximate Inference, Variational Inference, KL-Divergence, EM algorithm
(PRML) Ch9.Mixture Models and EM
K-means, GMM, EM algorithm, Variational Inference, Evidence Lower Bound
(PRML) Ch8.Graphical Models
PGM, Bayesian Network, Markov Random Fields
(PRML) Ch6.Kernel Methods
Kernel Function, Dual Representation, Radial Basis Function, Gaussian Process, Automatic Relevance Determination
(PRML) Ch5.Neural Networks
Neural Networks, Gradient Descent optimization, Hessian matrix, Bayesian Neural Network
(PRML) Ch4.Linear Models for Classification
Discriminant Function, Logistic Regression, Iterative Reweighted Least Squares, Laplace Approximation, Bayesian Logistic Regression
(PRML) Ch3.Linear Models for Regression
Basis Function, Kernel Function, Bayesian Linear Regression, Bayes Factor, Evidence
(PRML) Ch2.Probability Distributions
Distributions, Gaussian Distribution, Exponential Family, Noninformative Prior
(PRML) Ch1.Introduction
Probability, Bayesian Framework, Decision Theory, Information Theory