Bayesian experimental design for models with intractable likelihoods

Biometrics

Volumn 69, Issue 4, 2013, Pages 937-948

  Drovandi, Christopher C ^a   Pettitt, Anthony N ^a  

^a QUEENSLAND UNIVERSITY OF TECHNOLOGY   (Australia)

Author keywords

Approximate Bayesian computation; Bayesian experimental design; Markov chain Monte Carlo; Robust experimental design

Indexed keywords

BAYESIAN NETWORKS; CHAINS; DESIGN OF EXPERIMENTS; MARKOV PROCESSES; MONTE CARLO METHODS; PARAMETER ESTIMATION; STATISTICS; STOCHASTIC SYSTEMS;

APPROXIMATE BAYESIAN; APPROXIMATE BAYESIAN COMPUTATION; BAYESIAN COMPUTATION; BAYESIAN EXPERIMENTAL DESIGNS; MARKOV CHAIN MONTE CARLO; MARKOV CHAIN MONTE CARLO SAMPLINGS; MARKOV CHAIN MONTE-CARLO; PARAMETERS ESTIMATION; ROBUST EXPERIMENTAL DESIGNS; UTILITY FUNCTIONS;

STOCHASTIC MODELS;

ALGORITHM; BAYESIAN ANALYSIS; EXPERIMENTAL DESIGN; MARKOV CHAIN; MONTE CARLO ANALYSIS; NUMERICAL MODEL; PARAMETERIZATION;

APPROXIMATE BAYESIAN COMPUTATION; ARTICLE; AUTOMATED PATTERN RECOGNITION; BAYES THEOREM; BAYESIAN EXPERIMENTAL DESIGN; COMPUTER SIMULATION; MARKOV CHAIN MONTE CARLO; METHODOLOGY; PROBABILITY; ROBUST EXPERIMENTAL DESIGN; STATISTICAL ANALYSIS; STATISTICAL MODEL; STATISTICS;

APPROXIMATE BAYESIAN COMPUTATION; BAYESIAN EXPERIMENTAL DESIGN; MARKOV CHAIN MONTE CARLO; ROBUST EXPERIMENTAL DESIGN;

BAYES THEOREM; COMPUTER SIMULATION; DATA INTERPRETATION, STATISTICAL; LIKELIHOOD FUNCTIONS; MARKOV CHAINS; MODELS, STATISTICAL; PATTERN RECOGNITION, AUTOMATED; RESEARCH DESIGN; STOCHASTIC PROCESSES;

EID: 84890309642     PISSN: 0006341X     EISSN: 15410420     Source Type: Journal    
DOI: 10.1111/biom.12081     Document Type: Article

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