This technical note describes the construction of posterior probability maps that enable conditional or Bayesian inferences about regionally specific effects in neuroimaging. Posterior probability maps are images of the probability or confidence that an activation exceeds some specified threshold, given the data. Posterior probability maps (PPMs) represent a complementary alternative to statist...

Bayesian inference is a theoretically well-founded and conceptually simple approach to data analysis. The computations in practical problems are anything but simple though, and thus approximations are almost always a necessity. The topic of this thesis is approximate Bayesian inference and its applications in three intertwined problem domains. Variational Bayesian learning is one type of approx...

SUMMARY Suppose we want to estimate predictive probabilities for an unobserved random variable, whose distribution depends on an unknown nite-dimensional parameter which is estimated from past data. A crude \plug-in" approach is to substitute a point estimate of the unknown parameter into the predictive distribution. This may be criticized as failing to take into account the uncertainty of the ...

Bayesian Networks are gaining popularity as a graphical tool to communicate complex probabilistic reasoning required in the evaluation of DNA evidence. This study extends the current use of Bayesian Networks by incorporating the potential effects of evolution paternity calculations. Features of HUGIN (a software package used to create Bayesian Networks) are demonstrated that have not, as yet, b...

Recent work in Bayesian classifiers has shown that a better and more flexible representation of domain knowledge results in better classification accuracy. In previous work [1], we have introduced a new type of Bayesian classifier called Case-Based Bayesian Network (CBBN) classifiers. We have shown that CBBNs can capture finer levels of semantics than possible in traditional Bayesian Networks (...

We propose a novel method to solve a kidnapped robot problem. A mobile robot plans its sensor actions to localize itself using Bayesian network inference. The system differs from traditional methods such as simple Bayesian decision or top-down action selection based on a decision tree. In contrast, we represent the contextual relation between the local sensing results and beliefs about the glob...

The Bayesian approach has a number of attractive properties for forecasting uncertainty which have yet to be fully explored in the study of future population change. In this paper, we apply some simple Bayesian time series models to obtain future population estimates with uncertainty for England and Wales. Uncertainty in model choice is incorporated through Bayesian model averaging techniques. ...

Bayesian network is a powerful tool to represent and deal with uncertain knowledge. There exists much uncertainty in crop or animal disease. The construction of Bayesian network need much data and knowledge. But when data is scarce, some methods should be adopted to construct an effective Bayesian network. This paper introduces a disease diagnosis model based on Bayesian network, which is two-l...