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Bayesian Nonparametrics for Causal Inference and Missing Data - Chapman & Hall / CRC Monographs on Statistics and Applied Probability Daniels, Michael J. (University of Florida, Gainesville, USA)
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Bayesian Nonparametrics for Causal Inference and Missing Data - Chapman & Hall / CRC Monographs on Statistics and Applied Probability
Daniels, Michael J. (University of Florida, Gainesville, USA)
Bayesian nonparametric (BNP) methods can be used to flexibly model joint or conditional distributions, as well as functional relationships. These methods, along with causal and/or missingness assumptions, can be used with the g-formula to infer causal effects.
252 pages, 8 Tables, black and white; 42 Line drawings, black and white; 42 Illustrations, black and
| Mediji | Grāmatas Hardcover Book (Grāmata ar cieto muguriņu un vāku) |
| Izlaists | 2023. gada 23. augusts |
| ISBN13 | 9780367341008 |
| Izdevēji | Taylor & Francis Ltd |
| Lapas | 248 |
| Izmēri | 242 × 159 × 22 mm · 534 g |
| Valoda | Angļu |
