000826229 000__ 03392cam\a2200529Ii\4500
000826229 001__ 826229
000826229 005__ 20230306144351.0
000826229 006__ m\\\\\o\\d\\\\\\\\
000826229 007__ cr\cn\nnnunnun
000826229 008__ 180215s2018\\\\sz\\\\\\ob\\\\001\0\eng\d
000826229 019__ $$a1023734232$$a1024172898$$a1027053018$$a1029081603
000826229 020__ $$a9783319724256$$q(electronic book)
000826229 020__ $$a3319724258$$q(electronic book)
000826229 020__ $$z9783319724249
000826229 020__ $$z331972424X
000826229 0247_ $$a10.1007/978-3-319-72425-6$$2doi
000826229 035__ $$aSP(OCoLC)on1023424917
000826229 035__ $$aSP(OCoLC)1023424917$$z(OCoLC)1023734232$$z(OCoLC)1024172898$$z(OCoLC)1027053018$$z(OCoLC)1029081603
000826229 040__ $$aN$T$$beng$$erda$$epn$$cN$T$$dN$T$$dYDX$$dUPM$$dEBLCP$$dAZU$$dUAB$$dSTF$$dSNK$$dOCLCQ
000826229 049__ $$aISEA
000826229 050_4 $$aBF311
000826229 08204 $$a153$$223
000826229 1001_ $$aPalestro, James J.,$$eauthor.
000826229 24510 $$aLikelihood-free methods for cognitive science /$$cJames J. Palestro, Per B. Sederberg, Adam F. Osth, Trisha Van Zandt, Brandon M. Turner.
000826229 264_1 $$aCham :$$bSpringer,$$c2018.
000826229 300__ $$a1 online resource.
000826229 336__ $$atext$$btxt$$2rdacontent
000826229 337__ $$acomputer$$bc$$2rdamedia
000826229 338__ $$aonline resource$$bcr$$2rdacarrier
000826229 347__ $$atext file$$bPDF$$2rda
000826229 4901_ $$aComputational Approaches to Cognition and Perception,$$x2510-1889
000826229 504__ $$aIncludes bibliographical references and index.
000826229 5050_ $$aChapter 1. Motivation -- Chapter 2. Likelihood-Free Algorithms -- Chapter 3. A Tutorial -- Chapter 4. Validations -- Chapter 5. Applications -- Chapter 6. Conclusions -- Chapter 7. Distributions.
000826229 506__ $$aAccess limited to authorized users.
000826229 520__ $$aThis book explains the foundation of approximate Bayesian computation (ABC), an approach to Bayesian inference that does not require the specification of a likelihood function. As a result, ABC can be used to estimate posterior distributions of parameters for simulation-based models. Simulation-based models are now very popular in cognitive science, as are Bayesian methods for performing parameter inference. As such, the recent developments of likelihood-free techniques are an important advancement for the field. Chapters discuss the philosophy of Bayesian inference as well as provide several algorithms for performing ABC. Chapters also apply some of the algorithms in a tutorial fashion, with one specific application to the Minerva 2 model. In addition, the book discusses several applications of ABC methodology to recent problems in cognitive science. Likelihood-Free Methods for Cognitive Science will be of interest to researchers and graduate students working in experimental, applied, and cognitive science.
000826229 588__ $$aOnline resource; title from PDF title page (viewed February 20, 2018).
000826229 650_0 $$aCognitive science$$xMethodology.
000826229 7001_ $$aSederberg, Peter C.,$$d1943-$$eauthor.
000826229 7001_ $$aOsth, Adam F.,$$eauthor.
000826229 7001_ $$aVan Zandt, Trisha,$$eauthor.
000826229 7001_ $$aTurner, Brandon M.,$$eauthor.
000826229 77608 $$iPrint version:$$aPalestro, James J.$$tLikelihood-free methods for cognitive science.$$dCham : Springer, 2018$$z331972424X$$z9783319724249$$w(OCoLC)1012282300
000826229 830_0 $$aComputational Approaches to Cognition and Perception.
000826229 852__ $$bebk
000826229 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-319-72425-6$$zOnline Access$$91397441.1
000826229 909CO $$ooai:library.usi.edu:826229$$pGLOBAL_SET
000826229 980__ $$aEBOOK
000826229 980__ $$aBIB
000826229 982__ $$aEbook
000826229 983__ $$aOnline
000826229 994__ $$a92$$bISE