Journal Of The American Statistical Association V-28

By O. William. 1932

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Citations in the corpus

Concordance and discordance in cosmology (2018, background): “It is common in the literature to interpret the outcome on a Jeffreys’ scale [36, 37]: ln C < 0 indicates tension between the data sets and ln C > 0 agreement; 3 : 1 odds one way or the other is “substantial”, 10 : 1 is “strong”, 30 : 1 is “very strong”, 100 : 1 is “decisive”.”
Dark Energy Survey Year 1 results: Cosmological constraints from cosmic shear (2017, background): “(14) The interpretation of the Bayes factor can be characterized in multiple ways [137, 138].”
Parameter estimation for compact binaries with ground-based gravitational-wave observations using the LALInference software library (2014, background): “This allows us to check that our credible intervals are well calibrated, in the sense of [84].”
Sketching Techniques for Collaborative Filtering (2009, methodology): “Our analysis is based on Hoeffding’s inequality [Hoeffding, 1963].”
A Fast Algorithm for the Minimum Covariance Determinant Estimator (1999, methodology): “The MCD's better accuracy makes itvery useful as an initial estimate for one-step regression estimators (Simpson, Ruppert andCarroll 1992; Coakley and Hettmansperger 1993).”
Clustering algorithms: A comparative approach (2016, background): “In addition, the algorithm may fail to find very small clusters [29, 73].”
Bayesian coherent analysis of in-spiral gravitational wave signals with a detector network (2009, methodology): “Parameter estimation is usually dealt with using MCMC methods [33, 34], which may include advanced techniques such as parallel tempering [35, 36] and delayed rejection [37–40] to enhance the exploration of the parameter space.”
Stochastic blockmodels and community structure in networks (2010, background): “As early as 1987, Wang and Wong [16] proposed a stochastic blockmodel for directed simple graphs incorporating arbitrary expected inand out-degrees, along with a selection of other features.”
Characterizing large-scale quantum computers via cycle benchmarking (2019, background): “All the “approximately normal” statements in this section can be replaced by rigorous statements using the results of [36], Hoeffding’s inequality [32] and the union bound, at the expense of additional notation and less favorable (but pessimistic) constants.”
Modeling echo chambers and polarization dynamics in social networks (2019, background): “Within the traditional models based on constructive opinion dynamics [5], such unrestricted modes of interaction would eventually lead to a consensus, even on controversial issues.”
Efficient generation of large random networks. (2005, methodology): “We hence use the geometric method of [9] to skip over potential edges that are not”
Bayeswave: Bayesian inference for gravitational wave bursts and instrument glitches (2014, methodology): “It is possible to extend the RJMCMC analysis to higher Bayes Factors by imposing a false prior that weights the less favored model more heavily, then correct for the false prior when computing the Bayes Factor [33], but it gets increasingly difficult to achieve good mixing.”
Simulation of quantum circuits by low-rank stabilizer decompositions (2018, unknown): “Applying Hoeffding’s inequality [55] and using Eqs.”