Publications of the thesis
List of abbreviations
AI | Artificial intelligence |
BIC | Bayesian information criterion |
BLP | Bayesian logic program |
BP | Belief propagation (algorithm) |
EM | Expectation maximisation |
FA | Factor analysis |
HNFA | Hierarchical nonlinear factor analysis |
HMM | Hidden Markov model |
ICA | Independent component analysis |
ILP | Inductive logic programming |
KL | Kullback-Leibler (divergence) |
LOHMM | Logical Hidden Markov model |
MAP | Maximum a posteriori (estimate) |
ML | Maximum likelihood (estimate) |
MCMC | Markov chain Monte Carlo |
MLP | Multilayer perceptron (network) |
NDFA | Nonlinear dynamic factor analysis |
NMN | Nonlinear Markov network |
NRMN | Nonlinear relational Markov network |
NSSM | Nonlinear state-space model |
Probability density function | |
PoE | Product of experts |
PCA | Principal component analysis |
PRM | Probabilistic relational model |
RMN | Relational Markov network |
SRL | Statistical relational learning |
VB | Variational Bayesian |
List of symbols
And | |
Negation | |
Variables, events, or actions | |
Scalar variables | |
Probability of given | |
Probability density of given | |
Observations (or data) | |
Unknown variables | |
Model parameters | |
Latent variables | |
Utility of | |
Model structure and prior belief | |
Gaussian distribution of with a mean and a variance | |
Proportional to (or equals after normalisation) | |
Message sent away from root (belief propagation algorithm) | |
Message sent towards the root (belief propagation algorithm) | |
Potential in a Markov network | |
Approximation of the posterior distribution | |
Kullback-Leibler divergence between and | |
Observation (or data) vector for (time) index | |
Source (or factor) vector for (time) index | |
Auxiliary vector (either for control or variance modelling) | |
Mapping from the source space to the observation space | |
Mapping for modelling dynamics in the source space | |
Matrices belonging to parameters | |
Mean of the parameter in the approximating posterior distribution | |
Variance of the parameter in the approximating posterior distribution | |
Expectation over the distribution | |
Logical variables | |
Follows from (in logic programming) | |
Observed sequence of logical atoms |