In a communication network, point-to-point traffic volumes over time are critical for designing protocols that route information efficiently and for maintaining security, whether at the scale of an Internet service provider or within a corporation. While technically feasible, the direct measurement of point-to-point traffic imposes a heavy burden on network performance and is typically not impl...

A new language and inference algorithm for stochastic modeling is presented. This work refines and generalizes the stochastic functional language originally proposed by [1]. The language supports object-oriented representation and recursive functions. It provides a compact representation for a large class of stochastic models including infinite models. It provides the ability to represent gener...

MLF is a type system that seamlessly merges ML-style type inference with System-F polymorphism. We propose a system of graphic (type) constraints that can be used to perform type inference in both ML or MLF. We show that this constraint system is a small extension of the formalism of graphic types, originally introduced to represent MLF types. We give a few semantic preserving transformations o...

We discuss the problem of large scale grammatical inference in the context of the Tenjinno competition, with reference to the inference of deterministic finite state transducers, and discuss the design of the algorithms and the design and implementation of the program that solved the first problem. Though the OSTIA algorithm has good asymptotic guarantees for this class of problems, the amount ...

The paper presents a universal fault diagnostic expert system frame work. The frame work is characterized by two basic features. The first includes a fault diagnostic strategy which utilizes the fault classification and checks knowledge about unit under test. The degree of accuracy to which faults are located is improved by using fault classification knowledge. The second characteristic is obje...

Lexical acquisition is one of the most difficult problems in building up operational natural language processing systems. Automatic learning of new words (morphological, syntactic and semantic properties) is even harder. The paper discusses our solution for overcoming the lexical gaps. Learning what some unknown words might mean is abductively driven by the world knowledge and the local context...

We empirically evaluate a stochastic annealing strategy for Bayesian posterior optimization with variational inference. Variational inference is a deterministic approach to approximate posterior inference in Bayesian models in which a typically non-convex objective function is locally optimized over the parameters of the approximating distribution. We investigate an annealing method for optimiz...

MLF is a type system that seamlessly merges ML-style type inference with System-F polymorphism. We propose a system of graphic (type) constraints that can be used to perform type inference in both ML or MLF. We show that this constraint system is a small extension of the formalism of graphic types, originally introduced to represent MLF types. We give a few semantic preserving transformations o...

In this paper, we describe results to model lateral and longitudinal control behavior of drivers with simple linear multiple regression models. This approach fits into the Bayesian Programming (BP) approach (Bessière, 2008) because the linear multiple regression model suggests an action selection strategy which is an alternative to the BP action selection strategies draw and best. Furthermore, ...

Two approaches have been used to perform approximate inference in Bayesian networks for which exact inference is infeasible: employing an approximation algorithm, or approximating the structure. In this article we compare two structure-approximation techniques, edge-deletion and approximate structure learning based on sub-sampling, in terms of relative accuracy and computational efficiency. Our...