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LEADER 03372cam 2200541 i 4500
001
ocn920672225
003
OCoLC
005
20170818024827.0
008
150902s2016 flua b 001 0 eng
010
a| 2015032008
019
a| 913768190
020
a| 9781482253443
q| (hbk. ;
q| alk. paper)
020
a| 1482253445
q| (hbk. ;
q| alk. paper)
029
1
a| AU@
b| 000055397800
029
1
a| CHBIS
b| 010536489
029
1
a| CHDSB
b| 006496643
029
1
a| CHVBK
b| 358311454
029
1
a| CHVBK
b| 360288383
035
a| (Sirsi) o920672225
035
a| (OCoLC)920672225
z| (OCoLC)913768190
040
a| DLC
b| eng
e| rda
c| DLC
d| YDX
d| YDXCP
d| BDX
d| BTCTA
d| OCLCF
d| GZT
d| IDU
d| WURST
d| OCLCO
d| CHVBK
d| P4A
d| OCLCQ
d| Z5A
d| UtOrBLW
042
a| pcc
049
a| EREE
050
0
0
a| QA279.5
b| .M3975 2016
082
0
0
a| 519.5/42
2| 23
100
1
a| McElreath, Richard,
d| 1973-
=| ^A1343011
245
1
0
a| Statistical rethinking :
b| a Bayesian course with examples in R and Stan /
c| Richard McElreath, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany.
264
1
a| Boca Raton :
b| CRC Press/Taylor & Francis Group,
c| [2016]
300
a| xvii, 469 pages :
b| illustrations ;
c| 27 cm.
336
a| text
b| txt
2| rdacontent
337
a| unmediated
b| n
2| rdamedia
338
a| volume
b| nc
2| rdacarrier
490
1
a| Chapman & Hall/CRC texts in statistical science series
500
a| "A CRC title."
504
a| Includes bibliographical references and index.
505
0
a| The golem of Prague -- Small worlds and large worlds -- Sampling the imaginary -- Linear models -- Multivariate linear models -- Overfitting, regularization, and information criteria -- Interactions -- Markov chain Monte Carlo -- Big entropy and the generalized linear model -- Counting and classification -- Monsters and mixtures -- Multilevel models -- Adventures in covariance -- Missing data and other opportunities -- Horoscopes.
520
a| "Statistical Rethinking: A Bayesian Course with Examples in R and Stan builds readers' knowledge of and confidence in statistical modeling. Reflecting the need for even minor programming in today's model-based statistics, the book pushes readers to perform step-by-step calculations that are usually automated. This unique computational approach ensures that readers understand enough of the details to make reasonable choices and interpretations in their own modeling work. The text presents generalized linear multilevel models from a Bayesian perspective, relying on a simple logical interpretation of Bayesian probability and maximum entropy. It covers from the basics of regression to multilevel models. The author also discusses measurement error, missing data, and Gaussian process models for spatial and network autocorrelation." --Publisher's website.
650
0
a| Bayesian statistical decision theory.
=| ^A20878
650
0
a| R (Computer program language)
=| ^A523882
830
0
a| Texts in statistical science.
=| ^A370220
938
a| Brodart
b| BROD
n| 113315848
938
a| Baker and Taylor
b| BTCP
n| BK0017390543
938
a| YBP Library Services
b| YANK
n| 12520710
949
a| QA279.5 .M3975 2016
h| JOYNER48
o| jssb
i| 30372014717331
994
a| C0
b| ERE
596
a| 1
998
a| 4733195
999
a| QA279.5 .M3975 2016
w| LC
c| 1
i| 30372014717331
d| 8/12/2022
e| 3/9/2022
l| JGES
m| JOYNER
n| 11
r| Y
s| Y
t| JGESBK
u| 8/18/2017
x| BOOK
z| JSTACKS
o| .STAFF. jssb