About: Index set (recursion theory) is a research topic. Over the lifetime, 31 publications have been published within this topic receiving 348 citations.
TL;DR: In this article, a set of hierarchical iteration levels, one for each iteration of the recursive relation from which the data items are derived, is provided and all data items derived during a given iteration are associated with the corresponding iteration level.
Abstract: A structure and method of arranging recursively derived data items in a database. A set of hierarchical iteration levels, one for each iteration of the recursive relation from which the data items are derived, is provided and all data items derived during a given iteration are associated with the corresponding iteration level. Also provided is an index structure including an index set of non-leaf nodes, a sequence set of leaf nodes, and an iteration level index. The leaf nodes include a record of the iteration level of each data item. The data are globally linked according to iteration level or are clustered on pages which are linked according to iteration level. Highly efficient scan and search are implemented by utilizing the iteration level index and the record of iteration level in the leaf nodes to direct the scanning and searching to data generated during a single iteration. The least fixpoint of a set of mutually recursive relations is efficiently calculated by these methods.
TL;DR: In this article, a new class of distributional transformations is introduced, characterized by equations relating function weighted expectations of test functions on a given distribution to expectations of the transformed distribution on the test function's higher order derivatives.
Abstract: A new class of distributional transformations is introduced, characterized by equations relating function weighted expectations of test functions on a given distribution to expectations of the transformed distribution on the test function’s higher order derivatives. The class includes the size and zero bias transformations, and when specializing to weighting by polynomial functions, relates distributional families closed under independent addition, and in particular the infinitely divisible distributions, to the family of transformations induced by their associated orthogonal polynomial systems. For these families, generalizing a well known property of size biasing, sums of independent variables are transformed by replacing summands chosen according to a multivariate distribution on its index set by independent variables whose distributions are transformed by members of that same family. A variety of the transformations associated with the classical orthogonal polynomial systems have as fixed points the original distribution, or a member of the same family with different parameter.
TL;DR: In this article, a new class of distributional transformations is introduced, characterized by equations relating function weighted expectations of test functions on a given distribution to expectations of the transformed distribution on the test function's higher order derivatives.
Abstract: A new class of distributional transformations is introduced, characterized by equations relating function weighted expectations of test functions on a given distribution to expectations of the transformed distribution on the test function's higher order derivatives. The class includes the size and zero bias transformations, and when specializing to weighting by polynomial functions, relates distributional families closed under independent addition, and in particular the infinitely divisible distributions, to the family of transformations induced by their associated orthogonal polynomial systems. For these families, generalizing a well known property of size biasing, sums of independent variables are transformed by replacing summands chosen according to a multivariate distribution on its index set by independent variables whose distributions are transformed by members of that same family. A variety of the transformations associated with the classical orthogonal polynomial systems have as fixed points the original distribution, or a member of the same family with different parameter.
TL;DR: In this article, a model of a finite state machine (FSM) receiving a plurality of FSM inputs and producing a plurality FSM outputs is represented by a reduced-state trellis and a novel method is presented for updating soft decision information on the FSM input into higher confidence information.
Abstract: In a digital information processing system wherein a model of a finite state machine (FSM) receiving a plurality of FSM inputs and producing a plurality of FSM outputs is represented by a reduced-state trellis and wherein the FSM inputs are defined on a base closed set of symbols, a novel method is presented for updating soft decision information on the FSM inputs into higher confidence information whereby (1) the soft decision information is inputted in a first index set, (2) a forward recursion is processed on the input soft decision information based on the reduced-state trellis representation to produce forward state metrics, (3) a backward recursion is processed on the input soft decision information based on the reduced-state trellis representation to produce backward state metrics, wherein the backward recursion is independent of the forward recursion and (4) the forward state metrics and the backward state metrics are operated on to produce the higher confidence information.
TL;DR: In this paper, the authors discuss the method of alternating chains, i.e., a sequence of structures lying alternately in each of two given classes of structures, and a substantial number of the applications of these chains can be formulated in terms of separability (or interpolability) tests.
Abstract: Publisher Summary This chapter discusses the method of alternating chains. A common construction occurring in contributions to the theory of models is the development of an alternating chain of structures, i.e. of a sequence of structures lying alternately in each of two given classes of structures. Although not always originally formulated in these terms, it has turned out that a substantial number of the applications of these chains can be formulated in terms of separability (or interpolability) tests. The word “hierarchy” is usually used informally in mathematical discussions, so it no doubt suggests more or less structure to different people. By a family of sets we mean a function to a class of sets. “Hierarchy” is most often used in the literature in reference to a special kind of family of sets —namely a family of sets (or classes) of sets. The inclusion relation on the classes of sets induces a partial order on the domain (or index set) of the hierarchy, and in most cases this is a partial well order if not indeed a well order.