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Probabilistic data analysis: an introductory guide
Author(s) -
Skilling J.
Publication year - 1998
Publication title -
journal of microscopy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.569
H-Index - 111
eISSN - 1365-2818
pISSN - 0022-2720
DOI - 10.1046/j.1365-2818.1998.2780835.x
Subject(s) - probabilistic logic , computer science , prior probability , inference , algorithm , gibbs sampling , posterior probability , bayesian inference , set (abstract data type) , calculus (dental) , bayesian probability , artificial intelligence , machine learning , theoretical computer science , medicine , dentistry , programming language
Quantitative science requires the assessment of uncertainty, and this means that measurements and inferences should be described as probability 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 while the answer remains the same. However, probability calculus is only a language; it does not restrict the questions one can ask by setting one's prior. We discuss how to set sensible priors, in particular for a large problem like image reconstruction. We also introduce practical modern algorithms (Gibbs sampling, Metropolis algorithm, genetic algorithms, and simulated annealing) for computing probabilistic inference.