PROBABILISTIC DATA-ANALYSIS - AN INTRODUCTORY GUIDE

Quantitative science requires the assessment of uncertainty, and-this means that measurements and inferences should be described as probabil ity distributions. This is done by building data into a probabilistic likelihood function which produces a posterior 'answer' by modulating a prior 'question'. Probability calculus is the only way of doing this consistently, so that data can be included gradually or all at once w hile the answer remains the same. However. probability calculus is onl y a language: it does not restrict the questions one can ask by settin g one's prior. We discuss how to set sensible priors, in particular fo r a large problem like image reconstruction. We also introduce practic al modern algorithms (Gibbs sampling, Metropolis algorithm, genetic al gorithms, and simulated annealing) for computing probabilistic inferen ce.