The paper investigates asymptotically efficient inference in general time series likelihood models with time varying parameters. Inference procedures for general loss functions are evaluated by a weighted average risk criterion. The weight function focusses on persistent parameter paths of moderate magnitude, and is proportional to the distribution function of a Gaussian random walk. It is show...

Template dependencies (TD’s) are a class of data dependencies that include multivalued and join dependencies and embedded versions of these. A collection of techniques, examples and results about TD’s are presented. The principal results are: 1) Finite implication (implication over relations with a finite number of tuples) is distinct from unrestricted implication for TD’s. 2) There are, for TD...

We consider classification and regression tasks where we have missing data and assume that the (clean) data resides in a low rank subspace. Finding a hidden subspace is known to be computationally hard. Nevertheless, using a non-proper formulation we give an efficient agnostic algorithm that classifies as good as the best linear classifier coupled with the best low-dimensional subspace in which...

The study of dynamical models of HIV infection, based on a system of nonlinear ordinary differential equations (ODE), has considerably improved the knowledge of its pathogenesis. While the first models used simplified ODE systems and analyzed each patient separately, recent works dealt with inference in non-simplified models borrowing strength from the whole sample. The complexity of these mode...

Precise type information is invaluable for analysis and optimization of object-oriented programs. Some forms of polymorphism found in object-oriented languages pose significant difficulty for type inference, in particular data polymorphism. Agesen’s Cartesian Product Algorithm (CPA) can analyze programs with parametric polymorphism in a reasonably precise and efficient manner, but CPA loses pre...

This paper develops a precise polymorphic type inference algorithm. Several methods for polymorphic type inference have been developed, including let-polymorphism and flow-based approaches such as Agesen’s Cartesian Product Algorithm (CPA). In this paper we focus on the flow-based variety. There is a class of polymorphic behavior which CPA misses: so-called data polymorphism. In the context of ...

In this work, we show that fuzzy inference systems based on Similarity Based Reasoning (SBR) where the modification function is a fuzzy implication is a universal approximator under suitable conditions on the other components of the fuzzy system.

This primer presents parameter estimation methods common in Bayesian statistics and apply them to discrete probability distributions, which commonly occur in text modeling. Presentation starts with maximum likelihood and a posteriori estimation approaches and the full Bayesian approach. This presentation is completed by an overview of Bayesian networks, a graphical language to express probabili...

The reconstruction and synthesis of ancestral RNAs is a feasible goal for paleogenetics. This will require new bioinformatics methods, including a robust statistical framework for reconstructing histories of substitutions, indels and structural changes. We describe a "transducer composition" algorithm for extending pairwise probabilistic models of RNA structural evolution to models of multiple ...