000			01607 a2200217 4500
003			OSt
005			20220614105607.0
008			220606b xxu||||| |||| 00| 0 eng d
020			_a9781107603578
040			_cIIT Kanpur
041			_aeng
082			_a153.01519542 _bL514b
100			_aLee, Michael D.
245			_aBayesian cognitive modeling _ba practical course _cMichael D. Lee and Eric-Jan Wagenmakers
260			_bCambridge University Press _c2013 _aCambridge
300			_axiii, 264p
520			_aBayesian inference has become a standard method of analysis in many fields of science. Students and researchers in experimental psychology and cognitive science, however, have failed to take full advantage of the new and exciting possibilities that the Bayesian approach affords. Ideal for teaching and self-study, this book demonstrates how to do Bayesian modeling. Short, to-the-point chapters offer examples, exercises, and computer code (using WinBUGS or JAGS and supported by Matlab and R), with additional support available online. No advance knowledge of statistics is required and, from the very start, readers are encouraged to apply and adjust Bayesian analyses by themselves. The book contains a series of chapters on parameter estimation and model selection, followed by detailed case studies from cognitive science. After working through this book, readers should be able to build their own Bayesian models, apply the models to their own data, and draw their own conclusions.
650			_aBayesian statistical decision theory
700			_aWagenmakers, Eric-Jan
942			_cBK
999			_c565081 _d565081