Information Symmetries in Irreversible Processes

We study dynamical reversibility in stationary stochastic processes from an information theoretic perspective. Extending earlier work on the reversibility of Markov chains, we focus on finitary processes with arbitrarily long conditional correlations. In particular, we examine stationary processes represented or generated by edge-emitting, finite-state hidden Markov models. Surprisingly, we find pervasive temporal asymmetries in the statistics of such stationary processes with the consequence that the computational resources necessary to generate a process in the forward and reverse temporal directions are generally not the same. In fact, an exhaustive survey indicates that most stationary processes are irreversible. We study the ensuing relations between model topology in different representations, the process's statistical properties, and its reversibility in detail. A process's temporal asymmetry is efficiently captured using two canonical unifilar representations of the generating model, the forward-time and reverse-time epsilon-machines. We analyze example irreversible processes whose epsilon-machine presentations change size under time reversal, including one which has a finite number of recurrent causal states in one direction, but an infinite number in the opposite. From the forward-time and reverse-time epsilon-machines, we are able to construct a symmetrized, but nonunifilar, generator of a process---the bidirectional machine. Using the bidirectional machine, we show how to directly calculate a process's fundamental information properties, many of which are otherwise only poorly approximated via process samples. The tools we introduce and the insights we offer provide a better understanding of the many facets of reversibility and irreversibility in stochastic processes.Comment: 32 pages, 17 figures, 2 tables; http://csc.ucdavis.edu/~cmg/compmech/pubs/pratisp2.ht

The Past and the Future in the Present

We show how the shared information between the past and future---the excess entropy---derives from the components of directional information stored in the present---the predictive and retrodictive causal states. A detailed proof allows us to highlight a number of the subtle problems in estimation and analysis that impede accurate calculation of the excess entropy.Comment: 7 pages, 1 figure; http://cse.ucdavis.edu/~cmg/compmech/pubs/pafip.ht

Adapting modeling environments to domain specific interactions

Software tools are being used by experts in a variety of domains. There are numerous software modeling environments tailored to a specific domain expertise. However, there is no consistent approach to generically synthesize a product line of such modeling environments that also take into account the user interaction and experience adapted to the domain. The focus of my thesis is the proposal of a solution to explicitly model user interfaces and interaction of modeling environments so that they can be tailored to the habits and preferences of domain experts. We extend current model-driven engineering techniques that synthesize graphical modeling environments to also take interaction models into account. The formal semantics of our language framework is based on statecharts. We define a development process for generating such modeling environments to maximize reuse through a novel statechart refinement technique.Les outils logiciels sont utilisés par des experts dans une variété de domaines. Il existe de nombreux environnements de modélisation logicielle adaptés á une expertise spécifique. Cependant, il n’existe pas d’approche cohérente pour synthétiser génériquement une ligne de produits de tels environnements de modélisation qui prennent également en compte l’interaction et l’expérience utilisateur adaptées au domaine. L’objectif de ma thése est la proposition d’une solution pour modéliser explicitement les interfaces utilisateur et l’interaction des environnements de modélisation afin qu’ils puissent étre adaptés aux habitudes et aux préférences des experts du domaine. Nous étendons les techniques d’ingénierie actuelles pilotées par un modéle qui synthétisent des environnements de modélisation graphique pour prendre également en compte les modèles d’interaction. La sémantique formelle de notre cadre linguistique est basée sur des statecharts. Nous définissons un processus de développement pour générer de tels environnements de modélisation afin de maximiser la réutilisation à travers une nouveau technique de raffinement de statecharts