Results 41 to 50 of about 152 (62)

Identifying the consequences of dynamic treatment strategies: A decision-theoretic overview

open access: yes, 2010
We consider the problem of learning about and comparing the consequences of dynamic treatment strategies on the basis of observational data. We formulate this within a probabilistic decision-theoretic framework. Our approach is compared with related work
Dawid, A. Philip, Didelez, Vanessa
core   +1 more source

Multivariate log-concave distributions as a nearly parametric model [PDF]

open access: yes, 2017
In this paper we show that the family Pd(lc) of probability distributions on ℝd with log-concave densities satisfies a strong continuity condition. In particular, it turns out that weak convergence within this family entails (i) convergence in total ...
Dümbgen, Lutz   +2 more
core  

Extended statistical modeling under symmetry; the link toward quantum mechanics

open access: yes, 2012
We derive essential elements of quantum mechanics from a parametric structure extending that of traditional mathematical statistics. The basic setting is a set $\mathcal{A}$ of incompatible experiments, and a transformation group $G$ on the cartesian ...
Helland, Inge S.
core   +1 more source

Causal interpretation of stochastic differential equations

open access: yes, 2014
We give a causal interpretation of stochastic differential equations (SDEs) by defining the postintervention SDE resulting from an intervention in an SDE.
Hansen, Niels Richard, Sokol, Alexander
core   +1 more source

On the frequentist and Bayesian approaches to hypothesis testing [PDF]

open access: yes, 2006
Hypothesis testing is a model selection problem for which the solution proposed by the two main statistical streams of thought, frequentists and Bayesians, substantially differ.
Girón, F. Javier, Moreno, Elías
core   +2 more sources

Brittleness of Bayesian inference and new Selberg formulas [PDF]

open access: yes, 2014
The incorporation of priors in the Optimal Uncertainty Quantification (OUQ) framework \cite{OSSMO:2011} reveals brittleness in Bayesian inference; a model may share an arbitrarily large number of finite-dimensional marginals with, or be arbitrarily close
Owhadi, Houman, Scovel, Clint
core   +2 more sources

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