Stochastic Relations: Foundations for Markov Transition Systems

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ISBN 9781584889410
Cat# C9411
 

Features

  • Provides a self-contained introduction to Polish and analytic spaces, measures, selection theorems, and categories, including monads and Eilenberg–Moore algebras
  • Examines the interplay between probability theory and coalgebras
  • Presents a systematic treatment of the categorical aspects of the probability theory for Markov transition systems
  • Investigates bisimulations and logical and behavioral equivalence, promoting a better understanding of nondeterministic and randomized processes
  • Studies probabilistic interpretations of modal and temporal logics
  • Includes case studies of software architecture, the converse of a stochastic relation, and the average case analysis of two algorithms
  • Summary

    Collecting information previously scattered throughout the vast literature, including the author’s own research, Stochastic Relations: Foundations for Markov Transition Systems develops the theory of stochastic relations as a basis for Markov transition systems.

    After an introduction to the basic mathematical tools from topology, measure theory, and categories, the book examines the central topics of congruences and morphisms, applies these to the monoidal structure, and defines bisimilarity and behavioral equivalence within this framework. The author views developments from the general theory of coalgebras in the context of the subprobability functor. These tools show that bisimilarity and behavioral and logical equivalence are the same for general modal logics and for continuous time stochastic logic with and without a fixed point operator.

    With numerous problems and several case studies, this book is an invaluable study of an important aspect of computer science theory.

    Table of Contents

    Preface
    A Gentle Tutorial to All Things Considered
    Introduction
    Measurable Spaces
    Polish and Analytic Spaces
    Measurable Selectors
    Probability Measures
    Categories
    Stochastic Relations as Monads
    Introduction
    The Manes Monad
    The Giry Monad
    Case Study: Architectural Modeling through Monads
    Eilenberg–Moore Algebras for Stochastic Relations
    Introduction
    Characterization through Equivalence Relations
    Positive Convex Structures
    Algebras through Positive Convex Structures
    Examples
    The Left Adjoint
    The Existence of Semi-Pullbacks
    Introduction
    A Road Map
    Extending Semi-Pullbacks of Measures
    The Existence of Semi-Pullbacks
    Congruences and Bisimulations
    Introduction
    Smooth Equivalence Relations
    Factoring
    Bisimulations
    Behavioral Equivalence and a Portmanteau
    2-Bisimulations
    Simple Relations
    Case Study: The Converse of a Stochastic Relation
    Case Study: Simple Relations for Counting
    Interpreting Modal and Temporal Logics
    Introduction
    Modal Logics
    Projective Limits for Interpreting Temporal Logics
    F-Bisimulations for CSL
    Logical Equivalence for μCSL
    Appendix: Notations
    Categories
    Spaces
    Other
    Bibliography
    Index
    Bibliographic Notes appear at the end of each chapter.

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