An interesting development in automatic visual recognition has been the emergence of tasks where it is not possible to assign objective labels images, yet still feasible collect annotations that reflect human judgements about them. Machine learning-based predictors for these rely on supervised training models behavior annotators, i.e., what would average person's judgement be an image? A key op...

A new Bayesian modeling framework is proposed to account for the uncertainty in model parameters arising from and measurements errors, as well experimental, operational, environmental manufacturing variabilities. Uncertainty embedded using a single level hierarchy where uncertainties are quantified by Normal distributions with mean covariance treated hyperparameters. Unlike existing hierarchica...

Abstract Stellar obliquity, the angle between a planet’s orbital axis and its host star’s spin axis, traces formation evolution of planetary system. In transiting-exoplanet observations, only sky-projected stellar obliquity can be measured, but this deprojected using an estimate obliquity. paper, we introduce flexible, hierarchical Bayesian framework that used to infer distribution solely from ...

Hyperspectral images are corrupted by a combination of Gaussian-impulse noise. On one hand, the traditional approach handling denoising problem using maximum posteriori criterion is often restricted time-consuming iterative optimization process and design hand-crafted priors to obtain an optimal result. other discriminative learning-based approaches offer fast inference speed over trained model...

Quadrature is the problem of estimating intractable integrals, a problem that arises in many Bayesian machine learning settings. We present an improved Bayesian framework for estimating intractable integrals of specific kinds of constrained integrands. We derive the necessary approximation scheme for a specific and especially useful instantiation of this framework: the use of a log transformati...

Probabilistic graphical models are useful tools for modeling systems governed by probabilistic structure. Bayesian networks are one class of probabilistic graphical model that have proven useful for characterizing both formal systems and for reasoning with those systems. Probabilistic dependencies in Bayesian networks are graphically expressed in terms of directed links from parents to their ch...

Multi-agent systems (MAS) are groups of interacting intelligent software agents. An important application is sensor interpretation (SI) in sensor networks. SI domains are frequently modeled with Bayesian networks (BNs), and distributed versions of these problems can be modeled with distributed Bayesian networks (DBNs). The multiply sectioned Bayesian network (MSBN) framework is the most studied...

This paper presents a model-based monitoring framework for the detection of fatigue-related crack damages in plate-type structures commonly seen in aluminum ship hulls. The monitoring framework involves vibration-based damage detection methodologies and finite element modeling of continuum plate structures. A Bayesian-based damage detection approach is adopted for locating probable damage areas...

the use of techniques from Bayesian decision theory to address problems in AI. Decision theory provides a normative framework for representing and reasoning about decision problems under uncertainty. Within the context of this framework, researchers in uncertainty in the AI community have been developing computational techniques for building rational agents and representations suited to enginee...

-Possibilistic logic and Bayesian networks have provided advantageous methodologies and techniques for computerbased knowledge representation. This paper proposes a framework that combines these two disciplines to exploit their own advantages in uncertain and imprecise knowledge representation problems. The framework proposed is a possibilistic logic based one in which Bayesian nodes and their ...