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Structure and uncertainty: Graphical models for understanding complex data
Author(s) -
Best Nicky,
Green Peter
Publication year - 2005
Publication title -
significance
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.123
H-Index - 21
eISSN - 1740-9713
pISSN - 1740-9705
DOI - 10.1111/j.1740-9713.2005.00133.x
Subject(s) - graphical model , computer science , inference , probabilistic logic , variety (cybernetics) , data science , statistical inference , macro , statistical model , meaning (existential) , complex system , multivariate statistics , theoretical computer science , artificial intelligence , machine learning , epistemology , mathematics , statistics , programming language , philosophy
Statistics is fundamental to making sense of the complexity of modern science. From the micro‐level of the human genome to the macro‐level of the universe, scientists need statistical models to help them extract meaning from empirical observations. Graphical models have been used across a wide variety of disciplines for building multivariate probabilistic models to represent, and draw inference about, complex phenomena. Nicky Best and Peter Green explain the ideas behind graphical models and show how they can be used to help tackle the challenges of complex statistical problems.