title: Bayesian internal dosimetry calculations using Markov Chain Monte Carlo.
abstract: A new numerical method for solving the inverse problem of internal dosimetry is described. The new method uses Markov Chain Monte Carlo and the Metropolis algorithm. Multiple intake amounts, biokinetic types, and times of intake are determined from bioassay data by integrating over the Bayesian posterior distribution. The method appears definitive, but its application requires a large amount of computing time.
authors.affiliation: Los Alamos National Laboratory, NM 87545, USA. guthrie@lanl.gov
authors.forename: G
authors.initials: G
authors.lastname: Miller
authors.forename: H F
authors.initials: HF
authors.lastname: Martz
authors.forename: T T
authors.initials: TT
authors.lastname: Little
authors.forename: R
authors.initials: R
authors.lastname: Guilmette
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D001822:Body Burden; D003198:Computer Simulation; D006801:Humans; D016013:Likelihood Functions; D008390:Markov Chains; D008954:Models, Biological; D009010:Monte Carlo Method; D016273:Occupational Exposure; D011005:Plutonium / Q000493:pharmacokinetics / Q000652:urine; D011874:Radiometry / Q000379:methods / Q000706:statistics & numerical data*
chemical_list: D011005:Plutonium