Inference for discretely observed stochastic kinetic networks with applications to epidemic modeling

Biostatistics

Volumn 13, Issue 1, 2012, Pages 153-165

  Choi, Boseung ^a,b   Rempala, Grzegorz A ^a,b  

^a Daegu University   (South Korea)

^b MEDICAL COLLEGE OF GEORGIA   (United States)

Author keywords

Gibbs sampler; Kinetic constants; Maximum likelihood; SIR model; Stochastic kinetics network

Indexed keywords

ALGORITHM; ARTICLE; BAYES THEOREM; BIOSTATISTICS; EPIDEMIC; HUMAN; INFLUENZA; INFLUENZA VIRUS A H1N1; MONTE CARLO METHOD; PANDEMIC; PROBABILITY; STATISTICAL ANALYSIS; STATISTICAL MODEL; STATISTICS; UNITED STATES;

ALGORITHMS; BAYES THEOREM; BIOSTATISTICS; DATA INTERPRETATION, STATISTICAL; EPIDEMICS; HUMANS; INFLUENZA A VIRUS, H1N1 SUBTYPE; INFLUENZA, HUMAN; MARKOV CHAINS; MODELS, STATISTICAL; MONTE CARLO METHOD; PANDEMICS; STOCHASTIC PROCESSES; UNITED STATES;

EID: 83755163669     PISSN: 14654644     EISSN: 14684357     Source Type: Journal    
DOI: 10.1093/biostatistics/kxr019     Document Type: Article

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