Bayesian Data Analysis.

By T. Fearn, Andrew Gelman, John B. Carlin, Hal S. Stern, Donald B. Rubin. 1996

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Task-Agnostic Continual Learning Using Online Variational Bayes With Fixed-Point Updates (2020, methodology): “Practical variational Bayes for modern neural networks was first introduced by Graves (2011), where a parametric distribution was used to approximate the posterior distribution by minimizing the variational free energy.”
Stan: A Probabilistic Programming Language (2017, methodology): “This supplies fairly diffuse starting points when transformed back to the constrained scale, and thus help with convergence diagnostics as discussed in (Gelman et al. 2013).”
Fitting Linear Mixed-Effects Models Using lme4 (2014, methodology): “er values. In thissimpliﬁedcase,Uistheonlyquantityinthestatisticalmodelthatisbothrandomand unknown. Given this simpliﬁed linear mixed model, a standard result in Bayesian linear regression modelling (Gelman et al. 2013) implies that the conditional distribution of the spherical randomeﬀectsgiventheo”
Deep and Hierarchical Implicit Models (2017, background): “With probabilistic models, one typically posits a latent probability, and supposes each toss is a Bernoulli outcome given this probability (Murphy, 2012; Gelman et al., 2013).”
EDGE-MEDIATED DISPERSAL BEHAVIOR IN A PRAIRIE BUTTERFLY (2001, background): “2b), which is somewhat more widely described in probability and statistics texts (e.g., Gelman et al. 1995).”
A variational Bayes algorithm for fast and accurate multiple locus genome-wide association analysis (2010, background): “Since V-Bay is deterministic it does not need the long runs of Markov chains required by exact Bayesian MCMC algorithms [28].”
Dirichlet Process Mixtures of Generalized Linear Models (2009, methodology): “teria are met then 9: Record (z 1;:::;z n) and ( 1 ;:::; K ). 10: end if 11: until Mposterior samples obtained. 23 criteria for our numerical examples are given in Appendix sections A-5 to A-7. See Gelman et al. (2004) for a more complete discussion on convergence criteria. We can sample from the di”
Machine learning in cardiovascular flows modeling: Predicting pulse wave propagation from non-invasive clinical measurements using physics-informed deep learning (2019, methodology): “quantify uncertainty in the model predictions is to consider ensemble averaging of bootstrapped neural networks [27, 35]. Alternatively, Bayesian approaches based on Markov Chain Monte Carlo sampling [18] or generative models can be employed [64]. As examples, we refer the reader to the recent works”
Alternatives to statistical hypothesis testing in ecology: a guide to self teaching. (2006, methodology): “The new statistics encourage a tight linkage between theory and data in ecology by offering a unified approach to the evaluation of virtually all types of models—statistical and biological, linear and nonlinear.”