A Bayesian network (BN) is a graphical model of uncertainty that is especially well-suited to legal arguments. It enables us to visualise and model dependencies between different hypotheses and pieces of evidence and to calculate the revised probability beliefs about all uncertain factors when any piece of new evidence is presented. Although BNs have been widely discussed and recently used in the context of legal arguments there are numerous barriers that prevent more widespread use in the law. This presentation will review both the potential and barriers to using BNs within the law and will draw on real examples from recent murder cases in which the presenter acted as an expert witness.

Current neuroscience demands the development of new computational techniques based on machine learning methods. In this talk we will present some problems in the Cajal Blue Brain project that motivate the adaptation of Bayesian network learning approaches. These problems include: (a) neuroanatomy issues, like modeling and simulation of dendritic trees and classification of neuron types based on morphological features; (b) neurodegenerative diseases, like predicting health-related quality of life in Parkinson's disease and searching for genetic biomarkers in Alzheimer's disease.