Statistical Decision Theory and Bayesian Analysis, Second Edition

By James O. Berger. 1993

Subfields: Statistics and Probability, Probabilistic & Bayesian Methods

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Citations in the corpus

Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness (2020, background): “Furthermore, there exists p that minimize supp∗ s(p, p ∗|x) at every location of x ∈XOOD, then it minimizes the integral [7].”
Prediction-Based Decisions and Fairness: A Catalogue of Choices, Assumptions, and Definitions (2018, background): “This notion of fairness is connected to utility maximization by a set of results showing that, for a certain set of utility functions and scores ψ(v), the utility-maximizing decision rule δ is necessarily a single-threshold rule [68, 7, 19, 82].”
A Simple Baseline for Bayesian Uncertainty in Deep Learning (2019, methodology): “Berger [4] give the explanation for this as the Bernstein von Mises Theorem providing that in the limit the posterior will itself converge to a Gaussian.”
The Dataset Nutrition Label: A Framework To Drive Higher Data Quality Standards (2018, methodology): “Computed from an inferred generative model, this module allows for the full benefits of Bayesian analysis [33] , such as interpretability of inferences, coping with missing data, and robustness to outliers and regions of sparse data.”
BayesNAS: A Bayesian Approach for Neural Architecture Search (2019, methodology): “The hyper-priors for σ−2, λ and s are chosen to be a gamma distribution (Berger, 2013), i.”
The Uncertainty Bellman Equation and Exploration (2017, methodology): “In that spirit we develop another approximation of the posterior motivated by the Bayesian central limit theorem which states that, under some mild conditions, the posterior distribution converges to a Gaussian as the amount of data increases (Berger, 2013).”
A Model of Inductive Bias Learning (2000, methodology): “The earliest approaches to bias learning come from Hierarchical Bayesian methods in statistics (Berger, 1985; Good, 1980; Gelman, Carlin, Stern, & Rubim, 1995).”
On Invariance and Selectivity in Representation Learning (2015, background): “Also, invariant and selective representation are studied in statistics where they are referred to as maximal invariants [7,42].”
The Median Split: Robust, Refined, and Revived (2015, methodology): “Presumably the point of invoking Bayes was to demonstrate that large effect sizes can seem compelling.”
Design Issues for Generalized Linear Models: A Review (2006, methodology): “Several such approximations to the posterior are available ( Berger, 1985 ) and involve either the observed or the expected Fisher information matrix.”
MorePower 6.0 for ANOVA with relational confidence intervals and Bayesian analysis (2012, background): “It may seem intuitive that p (D | H 0 ) and p (H 0 | D) will be closely linked, but this is not necessarily the case (Berger, 1985; Wagenmakers, 2007).”
Marketing Analytics for Data-Rich Environments (2016, unknown): “Inference re ﬂ ects the researcher ’ s subjective uncertainty about the model and its parameters rather than random variation due to sampling (Berger 1985).”