000 03678nam a22005655i 4500
001 978-3-319-72425-6
003 DE-He213
005 20210201191335.0
007 cr nn 008mamaa
008 180207s2018 gw | s |||| 0|eng d
020 _a9783319724256
_9978-3-319-72425-6
050 4 _aBF201
072 7 _aJMR
_2bicssc
072 7 _aPSY008000
_2bisacsh
072 7 _aJMR
_2thema
082 0 4 _a153
_223
100 1 _aPalestro, James J.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aLikelihood-Free Methods for Cognitive Science
_h[electronic resource] /
_cby James J. Palestro, Per B. Sederberg, Adam F. Osth, Trisha Van Zandt, Brandon M. Turner.
250 _a1st ed. 2018.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2018.
300 _aXIV, 129 p. 27 illus., 7 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aComputational Approaches to Cognition and Perception,
_x2510-1889
500 _aAcceso multiusuario
505 0 _aChapter 1. Motivation -- Chapter 2. Likelihood-Free Algorithms -- Chapter 3. A Tutorial -- Chapter 4. Validations -- Chapter 5. Applications -- Chapter 6. Conclusions -- Chapter 7. Distributions.
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. .
541 _fUABC ;
_cTemporal ;
_d01/01/2021-12/31/2023.
650 0 _aCognitive psychology.
650 1 4 _aCognitive Psychology.
_0https://scigraph.springernature.com/ontologies/product-market-codes/Y20060
700 1 _aSederberg, Per B.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
700 1 _aOsth, Adam F.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
700 1 _aVan Zandt, Trisha.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
700 1 _aTurner, Brandon M.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783319724249
776 0 8 _iPrinted edition:
_z9783319724263
776 0 8 _iPrinted edition:
_z9783319891811
830 0 _aComputational Approaches to Cognition and Perception,
_x2510-1889
856 4 0 _zLibro electrónico
_uhttp://148.231.10.114:2048/login?url=https://doi.org/10.1007/978-3-319-72425-6
912 _aZDB-2-BSP
912 _aZDB-2-SXBP
942 _cLIBRO_ELEC
999 _c242338
_d242337