Generative probabilistic models extend the scope of inferential structure determination

Journal of Magnetic Resonance

Volumn 213, Issue 1, 2011, Pages 182-186

  Olsson, Simon ^a   Boomsma, Wouter ^b   Frellsen, Jes ^a   Bottaro, Sandro ^b   Harder, Tim ^a   Ferkinghoff Borg, Jesper ^b   Hamelryck, Thomas ^a  

^a UNIVERSITY OF COPENHAGEN   (Denmark)

^b TECHNICAL UNIVERSITY OF DENMARK   (Denmark)

Author keywords

Generative probabilistic models; Inferential structure determination; Sparse data

Indexed keywords

BAYESIAN INFERENCE; BIOMOLECULAR STRUCTURES; BOLTZMANN FACTORS; COMPUTATIONAL COSTS; CONVENTIONAL METHODS; EXPERIMENTAL DATA; FORCEFIELDS; FREE PARAMETERS; INFERENTIAL STRUCTURE DETERMINATION; NMR DATA; PRIOR DISTRIBUTION; PROBABILISTIC MODELS; PROTEIN STRUCTURES; RIGOROUS APPROACH; SPARSE DATA; STRUCTURE DETERMINATION;

BAYESIAN NETWORKS; HEURISTIC METHODS; INFERENCE ENGINES;

MODELS;

PROTEIN;

ALGORITHM; ARTICLE; BAYES THEOREM; CHEMICAL STRUCTURE; CHEMISTRY; ELECTROMAGNETIC FIELD; METHODOLOGY; NUCLEAR MAGNETIC RESONANCE; PROTEIN CONFORMATION; PROTEIN TERTIARY STRUCTURE; STATISTICAL MODEL; TEMPERATURE;

ALGORITHMS; BAYES THEOREM; ELECTROMAGNETIC FIELDS; MODELS, MOLECULAR; MODELS, STATISTICAL; NUCLEAR MAGNETIC RESONANCE, BIOMOLECULAR; PROTEIN CONFORMATION; PROTEIN STRUCTURE, TERTIARY; PROTEINS; TEMPERATURE;

EID: 80055079338     PISSN: 10907807     EISSN: 10960856     Source Type: Journal    
DOI: 10.1016/j.jmr.2011.08.039     Document Type: Article

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