Efficient parametric inference for stochastic biological systems with measured variability

Statistical Applications in Genetics and Molecular Biology

Volumn 13, Issue 3, 2014, Pages 379-390

  Johnston, Iain G ^a  

^a IMPERIAL COLLEGE LONDON   (United Kingdom)

Author keywords

Approximate Bayesian computation; Cellular noise; Parametric inference

Indexed keywords

RNA; MESSENGER RNA;

ALGORITHM; ARTICLE; BAYESIAN LEARNING; CALCULATION; CASE STUDY; CELL CYCLE; CELL DIVISION; DAUGHTER CELL; MATHEMATICAL MODEL; MEASUREMENT; NORMAL DISTRIBUTION; OBSERVATION; SIMULATION; STATISTICAL MODEL; STATISTICAL PARAMETERS; STOCHASTIC MODEL; BAYES THEOREM; BIRTH; DEATH; GENETICS; METABOLISM; MONTE CARLO METHOD; PROBABILITY; STATISTICS; SYSTEMS BIOLOGY;

ALGORITHMS; BAYES THEOREM; DEATH; LIKELIHOOD FUNCTIONS; MARKOV CHAINS; MONTE CARLO METHOD; PARTURITION; RNA, MESSENGER; STOCHASTIC PROCESSES; SYSTEMS BIOLOGY;

EID: 84902474441     PISSN: None     EISSN: 15446115     Source Type: Journal    
DOI: 10.1515/sagmb-2013-0061     Document Type: Article

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