On Conditional Decomposability

The requirement of a language to be conditionally decomposable is imposed on a specification language in the coordination supervisory control framework of discrete-event systems. In this paper, we present a polynomial-time algorithm for the verification whether a language is conditionally decomposable with respect to given alphabets. Moreover, we also present a polynomial-time algorithm to extend the common alphabet so that the language becomes conditionally decomposable. A relationship of conditional decomposability to nonblockingness of modular discrete-event systems is also discussed in this paper in the general settings. It is shown that conditional decomposability is a weaker condition than nonblockingness.Comment: A few minor correction

On the Skiadas ‘Conditional Preference Approach’ to Choice Under Uncertainty

We compare the Skiadas approach with the standard Savage framework of choice under uncertainty. At first glance, properties of Skiadas “conditional preferences” such as coherence and disappointment seem analogous to similarly motivated notions of decomposability and disappointment aversion defined on Savage “ex ante preferences.” We show, however, that coherence per se places almost no restriction on the structure of ex ante preferences. Coherence is an `external’ restriction across preferences whereas notions of decomposability in the Savage framework are ‘internal’ to the particular preference relation. Similarly, standard notions of disappointment aversion refer to ‘within act’ disappointments. Skiadas’s notion of disappointment aversion for families of conditional preference relations neither implies nor is implied by standard notions of disappointment aversion for ex ante preferences

Sampling decomposable graphs using a Markov chain on junction trees

Full Bayesian computational inference for model determination in undirected graphical models is currently restricted to decomposable graphs, except for problems of very small scale. In this paper we develop new, more efficient methodology for such inference, by making two contributions to the computational geometry of decomposable graphs. The first of these provides sufficient conditions under which it is possible to completely connect two disconnected complete subsets of vertices, or perform the reverse procedure, yet maintain decomposability of the graph. The second is a new Markov chain Monte Carlo sampler for arbitrary positive distributions on decomposable graphs, taking a junction tree representing the graph as its state variable. The resulting methodology is illustrated with numerical experiments on three specific models.Comment: 22 pages, 7 figures, 1 table. V2 as V1 except that Fig 1 was corrected. V3 has significant edits, dropping some figures and including additional examples and a discussion of the non-decomposable case. V4 is further edited following review, and includes additional reference