The Bayesian choice : from decision-theoretic foundations to computational implementation

By Christian P. Robert. 2007

1,460 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/9a1227f2fc93d22e13c649e97cfffafc97f3127e

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

VBA: A Probabilistic Treatment of Nonlinear Models for Neurobiological and Behavioural Data (2014, background): “These two problems correspond to two different objectives, which can be formalized in terms of statistical loss functions [12].”
Hands-On Bayesian Neural Networks—A Tutorial for Deep Learning Users (2020, background): “While there are strong theoretical principles and schema upon which this Bayes’ formula can be based [25], this section focuses on some practical beneﬁts of using BNNs.”
Fast Calibrated Additive Quantile Regression (2017, background): “Given that credible intervals can be derived within a decision-theoretic framework by adopting an appropriate loss function (see, e.g., Robert 2007, sec.”
Simultaneous estimation of the parameters of the Hurst–Kolmogorov stochastic process (2011, methodology): “Toeplitz matrix we can use the Levinson–Trench–Zohar algorithm as described in Musicus (1988) .”
Bayesian Compressive Sensing (2008, unknown): “As one may note, the Bayesian linear model considered in RVM is essentially one of the simplified models for Bayesian model selection [19]–[21].”
Penalising Model Component Complexity: A Principled, Practical Approach to Constructing Priors (2014, methodology): “We do not focus on the case where the hierarchical structure is added to increase the robustness of the model (See Chapter 10 of Robert, 2007, for a discussion of types of hierarchical structure). This additive model viewpoint is the key to understanding many of the choices we make, in particular th”
HESS Opinions "A random walk on water" (2009, background): “10 well as with the concept of probabilization of uncertainty, that is, the axiomatic reduction from the notion of an uncertain quantity to the notion of a random variable (Robert, 2007).”
Bayesian Estimation and Inference (2015, unknown): “An exemplary book is by Gill (2008), but many others are also very good (e.g., Bolstad, 2007; Christensen, Johnson, & Branscum, 2011; Gelman et al., 2004; Hoff, 2009; Jackman, 2009; Kruschke, 2011; Lee, 2004; Lynch, 2010; for concise pro-Bayesian arguments, see Robert, 2007, chap.”
The semiparametric Bernstein-von Mises theorem (2010, methodology): “From a practical point of view, the latter conclusion has an important implication: whereas it can be hard to compute optimal semiparametric confidence regions directly, simulation of a large sample from the marginal posterior (e.g., by MCMC techniques; see Robert [36]) is sometimes comparatively st”
Bayesian mixed-effects inference on classification performance in hierarchical data sets (2012, background): “This approach, however, is not without criticism (Robert, 2007).”
Inverse Problems in Heat Transfer (2009, background): “Finally, in section 2.6, directions for further investigation in the use of spectral methods for stochastic inverse problems are suggested.”
Model Averaging and Its Use in Economics (2017, background): “…prior with nice properties, such as invariance under rescaling and translation of the covariates (and more generally, invariant to reparameterization under affine transformations), and automatic adaption to situations with nearcollinearity between different covariates (Robert, 2007, p. 193).”