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) l Value and how changed for slevel (optional) 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 Source model S Source specification T Detector specification F Flow specification NMR model P Model and prior specification D Parameters and latent values Copyright (c) 1995-2007 by Radford M. Neal