نتایج جستجو برای: nested sampling techniques
تعداد نتایج: 848315 فیلتر نتایج به سال:
Priors in Bayesian analyses often encode informative domain knowledge that can be useful making the inference process more efficient. Occasionally, however, priors may unrepresentative of parameter values for a given dataset, which result inefficient space exploration, or even incorrect inferences, particularly nested sampling (NS) algorithms. Simply broadening prior such cases inappropriate im...
Abstract We present an improved version of the nested sampling algorithm nessai in which core is modified to use importance weights. In algorithm, samples are drawn from a mixture normalising flows and requirement for be independently identically distributed (i.i.d.) according prior relaxed. Furthermore, it allows added any order, likelihood constraint, evidence updated with batches samples. ca...
SUMMARY Nested sampling is a simulation method for approximating marginal likelihoods proposed by Skilling (2006). We establish that nested sampling has an approximation error that vanishes at the standard Monte Carlo rate and that this error is asymptotically Gaussian. We show that the asymptotic variance of the nested sampling approximation typically grows linearly with the dimension of the p...
We present the first application of a Nested Sampling algorithm to explore high-dimensional phase space particle collision events. describe adaptation algorithm, designed perform Bayesian inference computations, integration partonic scattering cross sections and generation individual events distributed according corresponding squared matrix element. As concrete example we consider gluon process...
We review state-of-the-art Monte Carlo (MC) techniques for computing fluid coexistence properties (Gibbs simulations) and adsorption simulations in nanoporous materials such as zeolites and metal–organic frameworks. Conventional MC is discussed and compared to advanced techniques such as reactive MC, configurational-bias Monte Carlo and continuous fractional MC. The latter technique overcomes t...
Nested sampling is a novel simulation method for approximating marginal likelihoods, proposed by Skilling (2007a,b). We establish that nested sampling leads to an error that vanishes at the standard Monte Carlo rate N−1/2, where N is a tuning parameter that is proportional to the computational effort, and that this error is asymptotically Gaussian. We show that the corresponding asymptotic vari...
Nested sampling is a Bayesian sampling technique developed to explore probability distributions localized in an exponentially small area of the parameter space. The algorithm provides both posterior samples and an estimate of the evidence (marginal likelihood) of the model. The nested sampling algorithm also provides an efficient way to calculate free energies and the expectation value of therm...
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