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DOI in the printed bibliography is fragmented by whitespace or line breaks. A longer candidate (10.1007/978-3-319-00032-935Savitsky) was visible in the surrounding text but could not be confirmed against doi.org as printed.
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Evidence text
Rossel, D., Telesca, D., & Johnson, V. E. (2013). Statistical models for data analysis. In P. Giudici, S. Ingrassia, & M. Vichi (Eds.), (pp. 305–313). Springer. doi: 10.1007/978-3-319-00032-935 Savitsky, T. (2010).Generalized Gaussian process models with Bayesian variable selection(Unpub- lished doctoral dissertation). Rice University. Savitsky, T., Vanucci, M., & Sha, N. (2011). Variable selection for nonparametric Gaussian process priors: Modelsandcomputationalstrategies.Statistical Science,26(1), 130–149. doi: 10.1214/ REVERSIBLE JUMP WITH NO REGRETS 29 11-STS354 Shaddox, E., Stingo, F. C., Peterson, C. B., Jacobson, S., Cruickshank-Quinn, C., Kechris, K., ... Vanucci, M. (2018). A Bayesian approach for learning gene networks underlying disease severity in COPD.Statistics in Biosciences,10, 59-85. doi: 10.1007/s12561-016-9176-6 Stingo, F. C., Guindani, M., Vanucci, M., & Calhoun, V. D. (2013). An integrative Bayesian modeling approach to imaging genetics.Journal of the American Statistical Association, 108(503), 876–891. doi: 10.1080/01621459.2013.804409 Tierney, L. (1994). Markov chains for exploring posterior distributions.The Annals of Statistics, 22(4), 1701–1762. doi: 10.1214/aos/1176325750 Tierney, L., Kass, R. E., & Kadane, J. B. (1989). Fully exponential Laplace approximations to expectations and variances of nonpositive functions.Journal of the American Statistical Association,84(407), 710–716. doi: 10.2307/2289652drton van den Bergh, D., Clyde, M. A., Gupta, A. R
Evidence payload
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