نتایج جستجو برای: nested sampling techniques

تعداد نتایج: 848315  

Journal: :Bayesian Analysis 2022

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...

Journal: :Machine learning: science and technology 2023

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...

2009
Nicolas Chopin

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...

Journal: :European Physical Journal C 2022

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...

Journal: :Monthly Notices of the Royal Astronomical Society 2011

Journal: :Journal of Open Source Software 2018

Journal: :EURASIP Journal on Advances in Signal Processing 2014

2013
David Dubbeldam Ariana Torres-Knoop Krista S. Walton

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...

2007
Nicolas Chopin Christian P. Robert

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...

Journal: :Biophysical journal 2012
Nikolas S Burkoff Csilla Várnai Stephen A Wells David L Wild

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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