The Geometry of Decision Theory
By Philip Dawid, Steffen L. Lauritzen. 2006
44 citations recorded by the source corpus.
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- A. P. Dawid
- Steffen L. Lauritzen
- Matthew Parry
- Philip Dawid
- Monica Musio
- Aapo Hyvärinen
- By
- Yoshua Bengio
Citations in the corpus
- Proper local scoring rules (2011, background): “For a proper scoring rule it is straightforward to show that the estimating equation (7) is unbiased [Dawid and Lauritzen (2005)] and, as a result, θ̂ is typically consistent, though not necessarily efficient; it may also display some degree of robustness.”
- Local proper scoring rules of order two (2011, methodology): “The Hyvärinen score is also proper [Dawid and Lauritzen (2005)], thus raising the question for a characterization of the local proper scoring rules of order k ≤ 2.”
- Theory and applications of proper scoring rules (2014, background): “In particular, the whole theory of information geometry, which subsumes but goes beyond the information metric on P , can be generalised to yield the decision geometry associated with a given PSR (Dawid and Lauritzen 2005; Dawid 2007).”
- The geometry of proper scoring rules (2007, background): “of the material was presented in Dawid and Lauritzen (2006) .”
- Making and Evaluating Point Forecasts (2009, background): “Technically, our discussion connects to M-estimation (Huber 1964; Huber and Ronchetti 2009) and quasi-likelihood (Wedderburn 1974; Dawid and Lauritzen 2006).”
- Some extensions of score matching (2007, methodology): “For the case of continuous-valued data, D = R, a new approach was proposed by Hyvärinen (2005). The new method, called score matching, completely avoids the computation of the normalization constant, but provably provides a estimator that is consistent.”
- PROPER LOCAL SCORING RULES ON DISCRETE SAMPLE SPACES (2011, methodology): “Clearly, the homogeneous local scoring rules investigated in Parry, Dawid and Lauritzen (2012) satisfy this requirement.”
- Minimum Scoring Rule Inference (2014, background): “For X ∼ P , where P might not belong to P, we can approximate P within P by PθP , where θP = argmin θ D(P, Pθ), (10) where D is the discrepancy associated with S.”