Bayesian semi parametric multi-state models. Statistical Modelling 2008; 8(2):169-198.Kneib T, Hennerfeind A. Bayesian semiparametric multi-state models, . Statistical Modelling, to appear 2008.Kneib T, Hennerf
Formal model selection via the marginal likelihood can be used to select between two or more competing models. However, models can also be assessed and criticised using measures of predictive performance. Here, we consider posterior predictive checks, as well as out-of-sample predictive performance....
We address this gap by developing causalBETA (Bayesian Event Time Analysis) - an open-source R package for estimating causal effects on event-time outcomes using Bayesian semiparametric models. The package provides a familiar front-end to users, with syntax identical to existing survival analysis R...
4.3 Multivariate Selection Models 101 4.4 Semiparametric Models 111 4.5 Conclusion 114 References 114 5 Modern Bayesian Factor Analysis 117 Hedibert Freitas Lopes 5.1 Introduction 117 5.2 Normal linear factor analysis 119 5.3 Factor stochastic volatility 125 5.4 Spatial factor analysis 128 5.5 Additional ...
Semiparametric latent factor models International Workshop on Artificial Intelligence and Statistics, PMLR (2005), pp. 333-340 View in ScopusGoogle Scholar Thebelt et al., 2021 Thebelt Alexander, Kronqvist Jan, Mistry Miten, Lee Robert M., Sudermann-Merx Nathan, Misener Ruth ENTMOOT: a framewo...
Snoek J (2013) Bayesian optimization and semiparametric models with applications to assistive technology. Dissertation, University of Toronto Tamaki H, Kita H, Kobayashi S (1996) Multi-objective optimization by genetic algorithms: a review. In: Proceedings of IEEE international conference on evolutionary...
Detecting differential gene expression with a semiparametric hierarchical mixture method. Biostatistics 5, 155–176 (2004). Article Google Scholar Kruschke, J. K. Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan (Academic Press, 2015). Abadi, M. et al. Tensorflow: Large-scale...
The selection of hybrids is an essential step in maize breeding. However, evaluating a large number of hybrids in field trials can be extremely costly. However, genomic models can be used to predict the expected performance of un-tested genotypes. Bayesi
A semiparametric approach for composite functional mapping of dynamic quantitative traits. Genetics 177: and the covariance matrix 1859–1870. Yang R, Tian Q, Xu S (2006). Mapping quantitative trait loci for longitudinal traits in line crosses. Genetics 173: 2339–2356. S^ jk " ¼1 þ ...
(when there are two models, but there could be more). The parameters within the models,𝜃andϕ, will in general be continuous but could be discrete. Thus the overall parameter space is a multi-dimensional space involvingM×𝜃×ϕ. Bayesian inference is re-allocation of credibility ...