A new time-domain probabilistic technique based on hierarchical Bayesian modeling (HBM) framework is proposed for calibration and uncertainty quantification of hysteretic type nonlinearities dynamical systems. Specifically, hyper models are introduced respectively material model parameters as well prediction error variance parameters, aiming to consider both the due unmodeled dynamics. asymptot...

Animal social networks are often constructed from point estimates of edge weights. In many contexts, weights inferred observational data, and the uncertainty around can be affected by various factors. Though this has been acknowledged in previous work, methods that explicitly quantify have not yet widely adopted remain undeveloped for common types data. Furthermore, existing unable to cope with...

Bayesian models have been applied to many areas of cognitive science including vision, language, and motor learning. We discuss the implications of this framework for cognitive development. We first present a brief introduction to the Bayesian framework. Bayesian models make assumptions about representation explicit, and provide a detailed account of learning. Furthermore, they can provide an a...

It is often claimed that the greatest value of the Bayesian framework in cognitive science consists in its unifying power. Several Bayesian cognitive scientists assume that unification is obviously linked to explanatory power. But this link is not obvious, as unification in science is a heterogeneous notion, which may have little to do with explanation. While a crucial feature of most adequate ...

Statistical modeling in a Bayesian framework applied to MRI This lecture formulates our statistical approach to tissue characterization in a Bayesian framework [10, 17]. The Bayesian approach is chosen because it is both expressive and flexible, and accounts for many promising and potentially useful clinical applications. In a broad sense the Bayesian paradigm is " to take prior beliefs about v...

In this paper, we propose a Bayesian framework, which constructs shared-state triphone HMMs based on a variational Bayesian approach, and recognizes speech based on the Bayesian prediction classification; variational Bayesian estimation and clustering for speech recognition (VBEC). An appropriate model structure with high recognition performance can be found within a VBEC framework. Unlike conv...

This thesis addresses machine learning techniques for Bayesian networks in the context of useradaptive systems. On the basis of the definition of a Bayesian network and the discussion of related methods and extensions of this framework, an overview of current research on the application of Bayesian networks within user-adaptive systems is presented. So far, there have been no major efforts to d...

Theories of decision making are often formulated in terms of deterministic axioms, which do not account for stochastic variation that attends empirical data. This study presents a Bayesian inference framework for dealing with fallible data. The Bayesian framework provides readily applicable statistical procedures addressing typical inference questions that arise when algebraic axioms are tested...

MacKay's Bayesian framework for backpropagation is a practical and powerful means of improving the generalisation ability of neural networks. The framework is reviewed and extended in a pedagogical way. The notation is simpliied using the ordinary weight decay parameter, and the noise parameter is shown to be nothing more than an overall scale. A detailed and explicit procedure for adjusting se...