Index
A
- ancestral sampling
- arm package
B
- basic sampling algorithms
- Bayes' rule
- Bayes' rule, in R
- Bayesian Linear models
- Bayesian Naive Bayes
- Bayesian theory
- bayesm package
- Bayes rule
- Bernoulli distribution
- Beta-Binomial
- Binomial distribution
- Blocked Nose / Graphs and conditional independence
- bnlearn R package
C
D
E
- empirical distribution
- Expectation Maximization (EM) algorithm
G
- gating function
- Gaussian mixture model
- glmnet package
- graphical model
- graphical models
- graphs
H
I
- importance sampling
- importance weight
J
- joint probability distribution, uncertainty
- junction tree
- junction tree algorithm
- Junction Tree Algorithm / Examples and applications
K
- Kullback-Leibler divergence
L
- L1-penalization (Lasso)
- L2 penalization
- Lasso
- Latent Dirichlet Allocation (LDA)
- latent variable models
- latent variables
- learning by inference
- likelihood / Interpreting the Bayes' formula
- linear regression
- log-likelihood
M
- machine learning
- Markov Chain Monte-Carlo (MCMC)
- Markov Chain Monte Carlo (MCMC)
- Markov Model
- maximum likelihood
- Maximum Likelihood Estimator (MLE)
- medical expert system
- Metropolis-Hastings algorithm
- mixing proportions
- mixture components
- mixture models
- mixture of Bernoulli
- mixture of experts
- ML algorithm
- model distribution
N
- Naive Bayes model
- Noisy-OR model
O
P
- packages, R
- parameter learning
- plate notation
- posterior distribution / Interpreting the Bayes' formula
- prior distribution / Interpreting the Bayes' formula
- probabilistic expert system
- probabilistic graphical models
- Probabilistic Graphical Models (PGM)
- probabilistic graphical models, examples
- probability calculus, uncertainty
- pseudo-random numbers
R
- R
- random variable
- random variables, uncertainty
- rejection sampling
- ridge regression
- RStan
S
T
U
V
- variable elimination
- variational inference
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