Results 61 to 70 of about 167,711 (260)
A hybrid sampler for Poisson-Kingman mixture models [PDF]
, 2015 This paper concerns the introduction of a new Markov Chain Monte Carlo scheme for posterior sampling in Bayesian nonparametric mixture models with priors that belong to the general Poisson-Kingman class. We present a novel compact way of representing the
Favaro, Stefano +2 more
core +2 more sources
, 2013 Appears in Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence (UAI2001)
de Freitas, N +3 more
openaire +3 more sources
Can boarding schools help looked after and vulnerable children improve academic attainment?
British Educational Research Journal, EarlyView.Abstract The education of children in statutory care, or at the edge of care, is a serious concern for governments and policymakers. How to promote educational opportunities for these children can involve challenging and often contentious proposals. In this paper, we study one proposal put into practice in England: the provision to children who are in ...
David Murphy +2 more
wiley +1 more source
Parameter estimation for X-ray scattering analysis with Hamiltonian Markov Chain Monte Carlo
Journal of Synchrotron Radiation, 2022 Bayesian-inference-based approaches, in particular the random-walk Markov Chain Monte Carlo (MCMC) method, have received much attention recently for X-ray scattering analysis.
Zhang Jiang +4 more
doaj +1 more source
Biological Diversity, EarlyView.Integrating data from plastid genomes, nrDNA, and 353 low‐copy nuclear genes, this study establishes a robust phylogenetic framework for Hylodesmum. This framework supports a taxonomic revision recognizing 18 species and reveals a complex pattern of bidirectional EA–ENA dispersal, with mammals as a plausible dispersal agent. ABSTRACT Phylogenomics with
Zhuqiu Song +5 more
wiley +1 more source
sgmcmc: An R Package for Stochastic Gradient Markov Chain Monte Carlo
Journal of Statistical Software, 2019 This paper introduces the R package sgmcmc; which can be used for Bayesian inference on problems with large data sets using stochastic gradient Markov chain Monte Carlo (SGMCMC).
Jack Baker +3 more
doaj +1 more source
Adaptive MCMC with online relabeling
, 2015 When targeting a distribution that is artificially invariant under some permutations, Markov chain Monte Carlo (MCMC) algorithms face the label-switching problem, rendering marginal inference particularly cumbersome. Such a situation arises, for example,
Bardenet, Rémi +3 more
core +3 more sources
Biological Diversity, EarlyView.Plastome and microsatellite data reveal strong population structure but low genetic diversity in the fully mycoheterotrophic herb Burmannia nepalensis across subtropical China. Multiple glacial refugia and recent population decline highlight the roles of geographic isolation, climatic history, and human disturbance in shaping the biodiversity of ...
Miaomiao Shi +8 more
wiley +1 more source
Credit risk clustering in a business group: Which matters more, systematic or idiosyncratic risk?
Cogent Economics & Finance, 2019 Understanding how defaults correlate across firms is a persistent concern in risk management. In this paper, we apply covariate-dependent copula models to assess the dynamic nature of credit risk dependence, which we define as “credit risk clustering ...
Feng Li, Zhuojing He
doaj +1 more source
Reconstructing Probability Distributions with Gaussian Processes
, 2019 Modern cosmological analyses constrain physical parameters using Markov Chain Monte Carlo (MCMC) or similar sampling techniques. Oftentimes, these techniques are computationally expensive to run and require up to thousands of CPU hours to complete.
McClintock, Thomas, Rozo, Eduardo
core +1 more source