Results 91 to 100 of about 132,359 (288)
Phenotypic Subtypes of Obstructive Eustachian Tube Dysfunction as Defined by Cluster Analysis
The Laryngoscope, EarlyView.Obstructive ETD encompasses five clinically distinct phenotypes, ranging from mild, post‐upper respiratory infection presentations to chronic, bilateral disease driven by sinusitis and reflux. These were identified through hierarchical cluster analysis of 490 patients using seven key clinical variables.
Jenilkumar H. Patel +4 more
wiley +1 more source
On the Markov Chain Monte Carlo (MCMC) method
Sadhana, 2006 Let \(f(x)\) be a density of a distribution of some random variable \(X.\) We are interested in computing the integral \(\int\limits g(x) f(x)\,dx = E g(X)\) for a given function \(g.\) If we can generate a random sample \(x_1, \ldots, x_n\) of size \(n\) from this distribution and compute \(a_n ={1\over n} \sum_{i=1}^n g(x_i)\), then by the law of ...
openaire +3 more sources
A splitting method to reduce MCMC variance
, 2020 We explore whether splitting and killing methods can improve the accuracy of Markov chain Monte Carlo (MCMC) estimates of rare event probabilities, and we make three contributions. First, we prove that "weighted ensemble" is the only splitting and killing method that provides asymptotically consistent estimates when combined with MCMC. Second, we prove
Webber, Robert J. +2 more
openaire +2 more sources
Mathematical Methods in the Applied Sciences, EarlyView.We develop a full randomization of the classical hyper‐logistic growth model by obtaining closed‐form expressions for relevant quantities of interest, such as the first probability density function of its solution, the time until a given fixed population is reached, and the population at the inflection point.
Juan Carlos Cortés +2 more
wiley +1 more source
Detection of the quality of vital signals by the Monte Carlo Markov Chain (MCMC) method and noise deleting. [PDF]
Health Inf Sci Syst, 2021 Vajargah KF, Benis SG, Golshan HM.
europepmc +1 more source
Scalable Rejection Sampling for Bayesian Hierarchical Models [PDF]
, 2014 Bayesian hierarchical modeling is a popular approach to capturing unobserved heterogeneity across individual units. However, standard estimation methods such as Markov chain Monte Carlo (MCMC) can be impracticable for modeling outcomes from a large ...
Braun, Michael, Damien, Paul
core
Nordic Journal of Botany, EarlyView.The Irano‐Turanian Floristic Region harbors a rich flora, but our understanding of the development of this diversity is limited by a lack of data on phylogenetic relationships and biogeographic patterns of endemic and more widespread plants. Hypotheses of in situ diversification versus allopatric diversification were tested using Iris subgen. Scorpiris,
Mona Salimbahrami +4 more
wiley +1 more source
Infectious Disease ModellingDisease forecasting and surveillance often involve fitting models to a tremendous volume of historical testing data collected over space and time. Bayesian spatio-temporal regression models fit with Markov chain Monte Carlo (MCMC) methods are commonly ...
Rongjie Huang +5 more
doaj +1 more source
Phase randomisation: a convergence diagnostic test for MCMC [PDF]
Most MCMC users address the convergence problem by applying diagnostic tools to the output produced by running their samplers. Potentially useful diagnostics may be borrowed from diverse areas such as time series.
Darfiana Nur +2 more
core +1 more source
Using MCMC methods in some application problems
International Journal of Scientific World, 2014 MCMC methods are very important tools for estimating unknown parameters in Bayesian models. Especially in the case of high dimensions. Gaussian mixture model is one of the applications of estimating hyper parameters by MCMC method. Keywords : Gibbs Sampling, Slice Sampling, Metropolis-Hastings Algorithm, Gaussian, Mixture Model.
Parvin Azhdari +2 more
openaire +2 more sources