TL;DR: It is shown analytically that the multitarget multiBernoulli (MeMBer) recursion, proposed by Mahler, has a significant bias in the number of targets and to reduce the cardinality bias, a novel multi Bernoulli approximation to the multi-target Bayes recursion is derived.
Abstract: It is shown analytically that the multitarget multiBernoulli (MeMBer) recursion, proposed by Mahler, has a significant bias in the number of targets. To reduce the cardinality bias, a novel multiBernoulli approximation to the multi-target Bayes recursion is derived. Under the same assumptions as the MeMBer recursion, the proposed recursion is unbiased. In addition, a sequential Monte Carlo (SMC) implementation (for generic models) and a Gaussian mixture (GM) implementation (for linear Gaussian models) are proposed. The latter is also extended to accommodate mildly nonlinear models by linearization and the unscented transform.
TL;DR: This work suggests a novel explanation of people's limited recursive performance, without assuming the existence of a mentally represented competence grammar allowing unbounded recursion.
TL;DR: This paper describes a simple and powerful query language based on pattern matching and shows that it can be expressed using structural recursion, which is introduced as a top-down, recursive function, similar to the way XSL is defined on XML trees.
Abstract: This paper presents structural recursion as the basis of the syntax and semantics of query languages for semistructured data and XML. We describe a simple and powerful query language based on pattern matching and show that it can be expressed using structural recursion, which is introduced as a top-down, recursive function, similar to the way XSL is defined on XML trees. On cyclic data, structural recursion can be defined in two equivalent ways: as a recursive function which evaluates the data top-down and remembers all its calls to avoid infinite loops, or as a bulk evaluation which processes the entire data in parallel using only traditional relational algebra operators. The latter makes it possible for optimization techniques in relational queries to be applied to structural recursion. We show that the composition of two structural recursion queries can be expressed as a single such query, and this is used as the basis of an optimization method for mediator systems. Several other formal properties are established: structural recursion can be expressed in first-order logic extended with transitive closure; its data complexity is PTIME; and over relational data it is a conservative extension of the relational calculus. The underlying data model is based on value equality, formally defined with bisimulation. Structural recursion is shown to be invariant with respect to value equality.
TL;DR: The structure of the termination proofs, the selection of well-founded orderings, norms and level mappings, the inference of interargument relations, and special treatments proposed for dealing with mutual recursion are outlined.
Abstract: We survey termination analysis techniques for Logic Programs. We give an extensive introduction to the topic. We recall several motivations for the work, and point out the intuitions behind a number of LP-specific issues that turn up, such as: the study of different classes of programs and LP languages, of different classes of queries and of different selection rules, the difference between existential and universal termination, and the treatment of backward unification and local variables. Then, we turn to more technical aspects: the structure of the termination proofs, the selection of well-founded orderings, norms and level mappings, the inference of interargument relations, and special treatments proposed for dealing with mutual recursion. For each of these, we briefly sketch the main approaches presented in the literature, using a fixed example as a file rouge. We conclude with some comments on loop detection and cycle unification and state some open problems.
TL;DR: A linear type system with recursion operators for inductive datatypes which ensures that all definable functions are polynomial time computable and improves upon previous such systems in that recursive definitions can be arbitrarily nested.
Abstract: We propose a linear type system with recursion operators for inductive datatypes which ensures that all definable functions are polynomial time computable. The system improves upon previous such systems in that recursive definitions can be arbitrarily nested, in particular no predicativity or modality restrictions are made.