Statistical Decision Theory

By Simon French, David Ríos Insua. Kendall's library of statistics, 2000

636 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/c4046d27ec9bfda00690bec792412f9da2267c91

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Citations in the corpus

Causal analysis with Chain Event Graphs (2010, background): “In the related field of decision analysis, French and Insua [11] argue that the advantages of influence diagrams over decision trees are illusory, and point out that asymmetric problems in which a particular choice of action at a decision node makes available different choices of action at subsequen”
Information, Divergence and Risk for Binary Experiments (2009, background): “(2005) is dated 13 October, 2005, Liese and Vajda (2006) was received on 26 October 2005 and Buja et al.”
From optimal transport to generative modeling: the VEGAN cookbook (2017, background): “The class of f -divergences [8] is deﬁned by D f ( P (cid:107) Q ) := f p ( x ) q ( x ) q ( x ) dx , where f : (0 , ∞ ) → R is any convex function satisfying f (1) = 0.”
Renyi Differential Privacy Mechanisms for Posterior Sampling (2017, methodology): “(Corollary 1.19 from [11]) For minimal exponential family, the Hessian of the log-partition function ∇ 2 C ( η ) is nonsingular for every parameter η in the interior of the normalizable set E .”
Wasserstein Auto-Encoders (2017, background): “The class of f -divergences [9] is defined by Df (PX‖PG) := ∫ f (pX(x) pG(x) ) pG(x)dx, where f : (0,∞)→ R is any convex function satisfying f(1) = 0.”
AdaGAN: Boosting Generative Models (2017, background): “In this case the integral is understood as Df (Q‖P ) = ∫ f(dQ/dP )1[dP (x)>0,dQ(x)>0]dP (x) + f(0)P (dQ = 0) + f ◦(0)Q(dP = 0), where both f(0) and f◦(0) may take value ∞ [15].”
Nonparametric calibration for age estimation (2002, background): “This type of problem is described by Berger (1985), section 3.2, who suggested determining the central and tail parts of the prior separately.”
Informativeness of genetic markers for inference of ancestry. (2003, background): “Under the assumption of a uniform prior on the population of origin, or for all ‫(ސ‬Q p i) p 1/K i, the aim is to choose an assignment rule, or a set of values of , that minimizes the expected value of the d ij loss ( Weiss 1961, p. 69), or K N K p ij r(d) p d c .”
Planning algorithms (2006, methodology): “, [91]) and in some artificial intelligence books (e.”