Search results for "Bayesian evidence"

showing 3 items of 3 documents

Testing LTB void models without the cosmic microwave background or large scale structure: new constraints from galaxy ages

2012

We present new observational constraints on inhomogenous models based on observables independent of the CMB and large-scale structure. Using Bayesian evidence we find very strong evidence for homogeneous LCDM model, thus disfavouring inhomogeneous models. Our new constraints are based on quantities independent of the growth of perturbations and rely on cosmic clocks based on atomic physics and on the local density of matter.

AstrofísicaVoid (astronomy)Cosmology and Nongalactic Astrophysics (astro-ph.CO)dark energy experimentsCosmic microwave backgroundgalaxy evolutionFOS: Physical sciencesAstrophysicsBayesian evidenceAstrophysics::Cosmology and Extragalactic AstrophysicsAstrophysics01 natural sciences0103 physical sciencesScale structuredark energy theory010303 astronomy & astrophysicsPhysicsCOSMIC cancer databaseCosmologia010308 nuclear & particles physicsAstronomy and AstrophysicsObservableGalaxiesGalaxyGalàxiesCosmologyHomogeneousAstrophysics - Cosmology and Nongalactic Astrophysics
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Recent Advances in Bayesian Inference in Cosmology and Astroparticle Physics Thanks to the MultiNest Algorithm

2012

We present a new algorithm, called MultiNest, which is a highly efficient alternative to traditional Markov Chain Monte Carlo (MCMC) sampling of posterior distributions. MultiNest is more efficient than MCMC, can deal with highly multi-modal likelihoods and returns the Bayesian evidence (or model likelihood, the prime quantity for Bayesian model comparison) together with posterior samples. It can thus be used as an all-around Bayesian inference engine. When appropriately tuned, it also provides an exploration of the profile likelihood that is competitive with what can be obtained with dedicated algorithms.

Astroparticle physicsPhysicsPosterior probabilitySampling (statistics)Markov chain Monte CarloBayesian evidenceBayesian inferenceCosmologyPrime (order theory)Statistics::Computationsymbols.namesakeSettore FIS/05 - Astronomia e AstrofisicasymbolsStatistics::MethodologyAlgorithmComputer Science::Databases
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Seed Activation Scheduling for Influence Maximization in Social Networks

2018

This paper addresses the challenge of strategically maximizing the influence spread in a social network, by exploiting cascade propagators termed “seeds”. It introduces the Seed Activation Scheduling Problem (SASP) that chooses the timing of seed activation under a given budget, over a given time horizon, in the presence/absence of competition. The SASP is framed as a blogger-centric marketing problem on a two-level network, where the decisions are made to buy sponsored posts from prominent bloggers at calculated points in time. A Bayesian evidence diffusion model – the Partial Parallel Cascade (PPC) model – allows the network nodes to be partially activated, proportional to their accumulat…

Mathematical optimizationsocial networksInformation Systems and ManagementOperations researchStrategy and ManagementScheduling (production processes)Time horizon02 engineering and technologyBayesian evidenceManagement Science and Operations Researchvaikutteetscheduling (computing)seed selectionsosiaaliset verkostot020204 information systemsvuoronnus0202 electrical engineering electronic engineering information engineeringEconomicsColumn generationta113influencesJob shop schedulingSocial networkbusiness.industryMaximizationmarkkinointimarketing020201 artificial intelligence & image processingbusinessOmega
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