
Uncertainty quantification through Monte Carlo method in a cloud computing setting
The Monte Carlo (MC) method is the most common technique used for uncert...
read it

Adaptive Monte Carlo Multiple Testing via MultiArmed Bandits
Monte Carlo (MC) permutation testing is considered the gold standard for...
read it

Mean shift cluster recognition method implementation in the nested sampling algorithm
Nested sampling is an efficient algorithm for the calculation of the Bay...
read it

On the Opportunities and Pitfalls of Nesting Monte Carlo Estimators
We present a formalization of nested Monte Carlo (NMC) estimation, where...
read it

Equivariant flowbased sampling for lattice gauge theory
We define a class of machinelearned flowbased sampling algorithms for ...
read it

Why Simple Quadrature is just as good as Monte Carlo
We motive and calculate NewtonCotes quadrature integration variance and...
read it

ChannelDriven Monte Carlo Sampling for Bayesian Distributed Learning in Wireless Data Centers
Conventional frequentist learning, as assumed by existing federated lear...
read it
Nested sampling for frequentist computation: fast estimation of small pvalues
We propose a novel method for computing pvalues based on nested sampling (NS) applied to the sampling space rather than the parameter space of the problem, in contrast to its usage in Bayesian computation. The computational cost of NS scales as log^21/p, which compares favorably to the 1/p scaling for Monte Carlo (MC) simulations. For significances greater than about 4σ in both a toy problem and a simplified resonance search, we show that NS requires orders of magnitude fewer simulations than ordinary MC estimates. This is particularly relevant for highenergy physics, which adopts a 5σ gold standard for discovery. We conclude with remarks on new connections between Bayesian and frequentist computation and possibilities for tuning NS implementations for still better performance in this setting.
READ FULL TEXT
Comments
There are no comments yet.