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| 005 | 20230206183325 | |
| 008 | 230206s2022 flua b 001 0 eng | |
| 010 | ▼a 2021037969 | |
| 020 | ▼a 9780367255398 ▼q (paperback) | |
| 020 | ▼a 9781032191591 ▼q (hardback) | |
| 020 | ▼z 9780429288340 ▼q (ebook) | |
| 035 | ▼a (KERIS)REF000019702255 | |
| 040 | ▼a DLC ▼b eng ▼e rda ▼c DLC ▼d DLC ▼d 211009 | |
| 042 | ▼a pcc | |
| 050 | 0 0 | ▼a QA279.5 ▼b .J64 2022 |
| 082 | 0 0 | ▼a 519.5/42 ▼2 23 |
| 084 | ▼a 519.542 ▼2 DDCK | |
| 090 | ▼a 519.542 ▼b J66b | |
| 100 | 1 | ▼a Johnson, Alicia A. |
| 245 | 1 0 | ▼a Bayes rules! : ▼b an introduction to Bayesian modeling with R / ▼c Alicia A. Johnson, Miles Ott, Mine Dogucu. |
| 260 | ▼a Boca Raton : ▼b CRC Press, ▼c 2022. | |
| 264 | 1 | ▼a Boca Raton : ▼b CRC Press, ▼c 2022. |
| 300 | ▼a xxi, 521 p. : ▼b ill. (some col.) ; ▼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 |
| 504 | ▼a Includes bibliographical references and index. | |
| 650 | 0 | ▼a Bayesian statistical decision theory. |
| 650 | 0 | ▼a R (Computer program language). |
| 700 | 1 | ▼a Ott, Miles Q., ▼e author. |
| 700 | 1 | ▼a Dogucu, Mine, ▼e author. |
| 830 | 0 | ▼a Chapman and Hall/CRC texts in statistical science. |
| 945 | ▼a ITMT |
소장정보
| No. | 소장처 | 청구기호 | 등록번호 | 도서상태 | 반납예정일 | 예약 | 서비스 |
|---|---|---|---|---|---|---|---|
| No. 1 | 소장처 중앙도서관/서고7층/ | 청구기호 519.542 J66b | 등록번호 111875901 | 도서상태 대출가능 | 반납예정일 | 예약 | 서비스 |
컨텐츠정보
목차
1 The Big (Bayesian) Picture 2 Bayes'' Rule 3 The Beta-Binomial Bayesian Model 4 Balance and Sequentiality in Bayesian Analyses 5 Conjugate Families 6 Approximating the Posterior 7 MCMC Under the Hood 8 Posterior Inference and Prediction 9 Simple Normal Regression 10 Evaluating Regression Models 11 Extending the Normal Regression Model 12 Poisson and Negative Binomial Regression 13 Logistic Regression 14 Naive Bayes Classification 15 Hierarchical Models are Exciting 16 (Normal) Hierarchical Models Without Predictors 17 (Normal) Hierarchical Models With Predictors 18 Non-Normal Hierarchical Regression & Classification 19 Adding More Layers
