  Inspired by empirical work in neuroscience for Bayesian approaches to brain
function, we give a unified probabilistic account of various types of symbolic
reasoning from data. We characterise them in terms of formal logic using the
classical consequence relation, an empirical consequence relation, maximal
consistent sets, maximal possible sets and maximum likelihood estimation. The
theory gives new insights into reasoning towards human-like machine
intelligence.
