LOG-TYPES: Types of log file records used by various programs.
Users do not usually need to know this information; it is mainly for
program maintainers. However, these types are visible in the output
of the log-records program (see log-records.doc).
CLASS OF PROGRAMS TYPE DESCRIPTION
* Any r State of random number generator
* Markov Chain Monte Carlo i Description of state/events at one iteration
o List of Monte Carlo operations to apply
s Explicitly set stepsizes (only some programs)
Dynamic MCMC p Values of "momentum" variables
t Specification of how to compute trajectories
Tempered MCMC m Schedule of temperatures and maybe biases
b Current temperature and associated state
Thermostated dynamics h Value of thermostat and corresponding momentum
* Specified distribution d Specification of distribution
q Values of variables ("position" variables)
* Bivariate Gaussian B Specification of bivariate Gaussian
X Point from bivariate Gaussian distribution
* Multivariate Gaussian M Specification of multivariate Gaussian
X Point from multivariate Gaussian distribution
* Molecular dynamics M Specifications of molecular dynamics system
Q Position coordinates of molecules
* Ising system I Specification of Ising system
S State of spins in system
D Direction to push
* Statistical modeling D Data specifications for training and test sets
M Model specification
V Characteristics of model for survival data
Neural network A Network architecture
F Flags modifying architecture
P Specification of priors for network
S Hyperparameters ("sigmas")
W Parameters of network ("weights")
Q Parameters of quadratic approximation
Gaussian process P Specification and priors
S Hyperparameters
F Case-by-case latent values
N Case-by-case noise variances
Mixture model P Specification and priors
S Hyperparameters
I Component indicators for training cases
O Offset parameters for components
N Noise variance parameters
Diffusion tree model P Specification and priors
S Hyperparameters and tree parameters
T Tree divergence times
R Parents of nodes in trees
L Latent vectors for training cases
N Locations of nodes in trees
NMR model P Model and prior specification
D Parameters and latent values
Copyright (c) 1995-2003 by Radford M. Neal