The multiply sectioned Bayesian network (MSBN) framework is the most studied approach for distributed Bayesian Network inference in an MAS setting. This paper describes a new framework that supports efficient approximate MASbased sensor interpretation, more autonomy and asynchrony among the agents, and more focused, situation-specific communication patterns. Its use can lead to significant impr...

The multiply sectioned Bayesian network (MSBN) framework is the most studied approach for distributed Bayesian Network inference in an MAS setting. This paper describes a new framework that supports efficient approximate MASbased sensor interpretation, more autonomy and asynchrony among the agents, and more focused, situation-specific communication patterns. Its use can lead to significant impr...

This paper is to present a Bayesian learning based framework for visual saliency detection in natural scenes. Especially, for any point in the scene, this framework has considered whether it is salient or not; but previous methods by Bayesian learning seem not to do so. This framework includes two steps. First, the framework indicates that visual saliency is constituted with three main saliency...

Parameter inference and model selection are very important for mathematical modeling in systems biology. Bayesian statistics can be used to conduct both parameter inference and model selection. Especially, the framework named approximate Bayesian computation is often used for parameter inference and model selection in systems biology. However, Monte Carlo methods needs to be used to compute Bay...

Bowers and Davis (2012) criticize Bayesian modelers for telling "just so" stories about cognition and neuroscience. Their criticisms are weakened by not giving an accurate characterization of the motivation behind Bayesian modeling or the ways in which Bayesian models are used and by not evaluating this theoretical framework against specific alternatives. We address these points by clarifying o...

Bayesian statistics provides a general framework for the treatment of problems involving uncertainty. A brief introduction to the principles of Bayesian analysis is presented. Special attention is given to the application of the Bayesian viewpoint in deterministic settings. Short reviews of the roles the Bayesian approach plays in areas of interpolation or \objective analysis" and data assimila...

Introduction: In recent times, Bayesian approaches have been increasingly popular in fMRI data analysis. One obvious appeal of the Bayesian approach is its interpretability. Instead of performing a hypothesis based inference at each voxel with an artificial threshold for declaration of activation, in the Bayesian approach we simply estimate the posterior probability of a voxel being active base...

This work relates the framework of model-based clustering for spatial functional data where the data are surfaces. We first introduce a Bayesian spatial spline regression model with mixed-effects (BSSR) for modeling spatial function data. The BSSR model is based on Nodal basis functions for spatial regression and accommodates both common mean behavior for the data through a fixed-effects part, ...