A Bayesian non-parametric Potts model with application to pre-surgical FMRI data

Statistical Methods in Medical Research

Volumn 22, Issue 4, 2013, Pages 364-381

  Johnson, Timothy D ^a   Liu, Zhuqing ^a   Bartsch, Andreas J ^b   Nichols, Thomas E ^c  

^a UNIVERSITY OF MICHIGAN   (United States)

^b UNIVERSITY OF HEIDELBERG   (Germany)

^c UNIVERSITY OF WARWICK   (United Kingdom)

Author keywords

decision theory; Dirichlet process; FMRI; hidden Markov random field; non parametric Bayes; Potts model

Indexed keywords

ADULT; ALGORITHM; BAYES THEOREM; CANCER SURGERY; CASE REPORT; CONFERENCE PAPER; DECISION THEORY; ELECTROENCEPHALOGRAM; FEMALE; FUNCTIONAL MAGNETIC RESONANCE IMAGING; FUNCTIONAL NEUROIMAGING; HUMAN; IMAGE ANALYSIS; IMAGE PROCESSING; MONTE CARLO METHOD; NONPARAMETRIC TEST; OLIGODENDROGLIOMA; POTTS MODEL; PREOPERATIVE EVALUATION; PROBABILITY; SIMULATION; STATISTICAL ANALYSIS; STATISTICAL MODEL; STATISTICAL PARAMETERS;

DECISION THEORY; DIRICHLET PROCESS; FMRI; HIDDEN MARKOV RANDOM FIELD; NON-PARAMETRIC BAYES; POTTS MODEL;

ALGORITHMS; BAYES THEOREM; BIOSTATISTICS; BRAIN NEOPLASMS; COMPUTER SIMULATION; DECISION THEORY; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; MARKOV CHAINS; MONTE CARLO METHOD; NORMAL DISTRIBUTION; STATISTICS, NONPARAMETRIC;

EID: 84881450950     PISSN: 09622802     EISSN: 14770334     Source Type: Journal    
DOI: 10.1177/0962280212448970     Document Type: Conference Paper

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