The abstract proposes a Bayesian joint quantile regression version of the multinomial probit model for choice data, estimable by Gibbs sampling, but the attached full text is a different paper on higher-derivative gravity.
Generation of spherically symmetric metrics in $f\left( R\right) $ gravity
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abstract
In $D-$dimensional spherically symmetric $f\left( R\right) $ gravity there are three unknown functions to be determined from the fourth order differential equations. It is shown that the system remarkably integrates to relate two functions through the third one to provide reduction to second order equations accompanied with a large class of potential solutions. The third function which acts as the generator of the process is $F\left( R\right) =\frac{df\left( R\right) }{dR}.$ We recall that our generating function has been employed as a scalar field with an accompanying self-interacting potential previously which is entirely different from our approach. Reduction of $f\left( R\right) $ theory into system of equations seems to be efficient enough to generate a solution corresponding to each generating function. As particular examples, besides known ones, we obtain new black hole solutions in any dimension $D$. We further extend our analysis to cover non-zero energy-momentum tensors. Global monopole and Maxwell sources are given as examples.
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Multinomial probit model based on joint quantile regression
The abstract proposes a Bayesian joint quantile regression version of the multinomial probit model for choice data, estimable by Gibbs sampling, but the attached full text is a different paper on higher-derivative gravity.