The slight perversion of the original title of this piece (The Future of the Bayesian Brain) reflects my attempt to write prospectively about 'Science and Stories' over the past 20 years. I will meet this challenge by dealing with the future and then turning to its history. The future of the Bayesian brain (in neuroimaging) is clear: it is the application of dynamic causal modeling to understan...

In this paper we consider the neuroscienti€c theory of the Bayesian brain in the light of adaptive web systems and content personalisation. In particular, we elaborate on neural mechanisms of human decision-making and the origin of lacking reliability of user feedback, o‰en denoted as noise or human uncertainty. To this end, we €rst introduce an adaptive model of cognitive agency in which popul...

The brain uses sensory information that is often uncertain in order to efficiently generate perceptual representations of the world. This observation has led to the Bayesian brain hypothesis, in which the brain combines internal expectations of the world with unreliable external sensory information in a nearly optimal probabilistic manner. Recent studies have suggested that to be true in statis...

conclusions there is no difference between median and paramedian approaches with respect to pdph incidence; the paramedian approach is therefore recommended, especially for older patients with degenerative changes in the spine and intervertebral spaces and those who cannot take the proper position. moreover, the rate of pdph was found to be significantly higher in females than in males. results...

The Bug resides in the software system causes new bugs damages the software reputation and increases the cost of bug fixing effort. So it is compulsory to know or predict the existence of bug as soon as possible and remove residing bugs timely. In this paper we reviewed the previously used approaches like Code Churn, Fine Grained Source Code Changes (SCC), Fine Grained Bug Severity Prediction a...