Bayesian Learning Based Optimization for Stochastic Logical System

Logical Stochastic Optimization

We present a logical framework to represent and reason about stochastic optimization problems based on probability answer set programming [Saad and Pontelli, 2006; Saad, 2006; Saad, 2007a]. This is established by allowing probability optimization aggregates, e.g., minimum and maximum in the language of probability answer set programming to allow minimization or maximization of some desired crit...

Learning Stochastic Logical Automaton

This paper is concerned with algorithms for the logical generalisation of probabilistic temporal models from examples. The algorithms combine logic and probabilistic models through inductive generalisation. The inductive generalisation algorithms consist of three parts. The first part describes the graphical generalisation of state transition models. State transition models are generalised by a...

Bayesian Learning of Logical Hidden

Efficient Bayesian Network Learning for System Optimization in Reliability Engineering

We present a new Bayesian network modeling that learns the behavior of an unknown system from real data and can be used for reliability engineering and optimization processes in industrial systems. The suggested approach relies on quantitative criteria for addressing the trade-off between the complexity of a learned model and its prediction accuracy. These criteria are based on measures from In...

Bayesian Stochastic Mesh Optimization for 3D reconstruction

We describe a mesh based approach to the problem of structure from motion. The input to the algorithm is a small set of images, sparse noisy feature correspondences (such as those provided by a Harris corner detector and cross correlation) and the camera geometry plus calibration. The output is a 3D mesh, that when projected onto each view, is visually consistent with the images. There are two ...