References in zbMATH (referenced in 927 articles )

Showing results 1 to 20 of 927.
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  1. Bauder, David; Bodnar, Taras; Parolya, Nestor; Schmid, Wolfgang: Bayesian inference of the multi-period optimal portfolio for an exponential utility (2020)
  2. Chua, Alvin J. K.: Sampling from manifold-restricted distributions using tangent bundle projections (2020)
  3. Flegg, Jennifer A.; Menon, Shakti N.; Byrne, Helen M.; McElwain, D. L. Sean: A current perspective on wound healing and tumour-induced angiogenesis (2020)
  4. Lv, Didi; Zhou, Qingping; Choi, Jae Kyu; Li, Jinglai; Zhang, Xiaoqun: Nonlocal TV-Gaussian prior for Bayesian inverse problems with applications to limited CT reconstruction (2020)
  5. Overstall, Antony; Mcgree, James: Bayesian design of experiments for intractable likelihood models using coupled auxiliary models and multivariate emulation (2020)
  6. Xie, Fangzheng; Xu, Yanxun: Adaptive Bayesian nonparametric regression using a kernel mixture of polynomials with application to partial linear models (2020)
  7. Yoder, Jordan; Chen, Li; Pao, Henry; Bridgeford, Eric; Levin, Keith; Fishkind, Donniell E.; Priebe, Carey; Lyzinski, Vince: Vertex nomination: the canonical sampling and the extended spectral nomination schemes (2020)
  8. Zhang, Tingting; Sun, Yinge; Li, Huazhang; Yan, Guofen; Tanabe, Seiji; Miao, Ruizhong; Wang, Yaotian; Caffo, Brian S.; Quigg, Mark S.: Bayesian inference of a directional brain network model for intracranial EEG data (2020)
  9. Abboud, Candy; Bonnefon, Olivier; Parent, Eric; Soubeyrand, Samuel: Dating and localizing an invasion from post-introduction data and a coupled reaction-diffusion-absorption model (2019)
  10. Ahonen, Ilmari; Nevalainen, Jaakko; Larocque, Denis: Prediction with a flexible finite mixture-of-regressions (2019)
  11. Antonelli, Joseph; Parmigiani, Giovanni; Dominici, Francesca: High-dimensional confounding adjustment using continuous Spike and Slab priors (2019)
  12. Aswani, Anil; Kaminsky, Philip; Mintz, Yonatan; Flowers, Elena; Fukuoka, Yoshimi: Behavioral modeling in weight loss interventions (2019)
  13. Athey, Susan; Tibshirani, Julie; Wager, Stefan: Generalized random forests (2019)
  14. Azzimonti, Laura; Corani, Giorgio; Zaffalon, Marco: Hierarchical estimation of parameters in Bayesian networks (2019)
  15. Baker, Ruth E.; Parker, Andrew; Simpson, Matthew J.: A free boundary model of epithelial dynamics (2019)
  16. Bayliss, C. D.; Fallaize, C.; Howitt, R.; Tretyakov, M. V.: Mutation and selection in bacteria: modelling and calibration (2019)
  17. Bornn, Luke; Shephard, Neil; Solgi, Reza: Moment conditions and Bayesian non-parametrics (2019)
  18. Branson, Zach; Rischard, Maxime; Bornn, Luke; Miratrix, Luke W.: A nonparametric Bayesian methodology for regression discontinuity designs (2019)
  19. Browning, Alexander P.; Haridas, Parvathi; Simpson, Matthew J.: A Bayesian sequential learning framework to parameterise continuum models of melanoma invasion into human skin (2019)
  20. Bunji, Kyosuke; Okada, Kensuke: Item response and response time model for personality assessment via linear ballistic accumulation (2019)

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