    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