... Maoris have the highest risk, followed by female Maoris, then ethnically European males, while ethnically European females have the lowest risk. event history frailty hazard models importance sampling left censoring maximum simulated likelihood estimation missing data Myocardial infarction random...

... an (approximate) sample from π. This is analogous to MCMC sampling, with t * being the burn-in period, the only difference being the need to simulate from the distribution of the process conditionally on its not having died. Control variates Importance sampling Killed Brownian motion...

... likelihood estimators where missing data are integrated out using Monte Carlo and importance sampling methods. We allow for random effects and integrate out this unobserved heterogeneity using a quadrature rule. In Monte Carlo experiments, we find that maximum simulated likelihood estimation is practically...

... intervals are reliable, and that our adaptive algorithm leads to reliable inference with little user specification. Adaptive algorithm Approximate Bayesian computation Confidence interval Importance sampling Markov chain Monte Carlo Tolerance choice Approximate Bayesian computation is a form...

.... These include data augmentation samplers for probit ( Albert & Chib, 1993 ) and logistic regression models ( Holmes & Held, 2006 ; Frühwirth-Schnatter & Frühwirth, 2010 ; Polson et al., 2013 ). Bayesian computation Importance sampling Markov chain Monte Carlo Metropolis–Hastings Posterior...

... faster than standard approaches, and in some cases, their accuracy rivals the best established estimators. Bayesian inference evidence importance sampling model selection variational Bayes In phylogenetic inference, the tree topology forms a key object of inference. In Bayesian phylogenetics...

... that captures the remaining intraday volatility dynamics. We analyze the model’s stochastic properties and extend it to capture leverage effects and overnight return information. The model is estimated by simulated maximum likelihood using efficient importance sampling. We apply the model to 30-min returns...

... Gibbs sampling Importance sampling Markov chain Monte Carlo sampling Point mass priors Engineering and Physical Sciences Research Council 10.13039/501100000266 EP/K014463/1 EP/K034154/1 Sampling from high dimensional probability distributions is a common task arising in many scientific areas...

... are uniformly distibuted within a giving polygon. The algorithm does not employ the rejection technique. Rather, it utilizes linear transformations to accomplish the task. Keywords: TRIANGULATION OF POLYGONS; LINEAR TRANSFORMATIONS; IMPORTANCE SAMPLING; EFFICIENCY OF REJECTION METHODS 1. INTRODUCTION A PAIR...

... Model ( https://dbpia.nl.go.kr/journals/pages/open_access/funder_policies/chorus/standard_publication_model ) SUMMARY Kernel density estimation techniques are used to smooth simulated samples from importance sampling function approximations to posterior distributions, resulting in revised...

...: BOOTSTRAP STATISTICS; CONFIDENCE INTERVAL INFLUENCE; IMPORTANCE SAMPLING; TESTING A PIVOTAL; TUNING AN ESTIMATOR 1. INTRODUCTION The bootstrap is a computer-based technique for estimating standard errors, biases, confidence intervals and other measures of statistical accuracy. It automatically produces...

... the category probabilities. Emphasis is placed on posterior inference about the finite population mean. Of independent interest is the methodology for evaluating the posterior moments and probabilities using Monte Carlo integration with importance sampling. dirichlet distribution monte carlo integration...

..., correctly quantifies uncertainty. Furthermore, for such a choice of bandwidth we can implement an importance sampling algorithm to sample from the posterior whose acceptance probability tends to unity as the data sample size increases. This compares favourably to results for standard approximate Bayesian...

... in this way, we can simulate samples from it using standard Monte Carlo methods. One approach, which we will focus on later, uses importance sampling. Let . Given a proposal density , a bandwidth , and a Monte Carlo sample size , an importance sampler would proceed as in Algorithm 1. The set...

... the computational cost is effectively proportional to and in the second there is a considerable start-up cost at each iteration. Importance sampling Pseudo-marginal Markov chain Monte Carlo The Metropolis–Hastings algorithm is often used to approximate expectations with respect to posterior distributions...

...Yujin Chung; Jody Hey isolation-with-migration model importance sampling Markov chain representation model comparison likelihood ratio test Abstract We present a new Bayesian method for estimating demographic and phylogenetic history using population genomic data. Several key innovations...

...-locus Moran model sampling probability importance sampling THE coalescent with recombination ( Griffiths and Marjoram 1997 ) provides a basic population genetic model for recombination. For a very small number of loci and a constant population size, the likelihood (or sampling probability) can...

...S. A. Kharroubi; T. J. Sweeting These formulae all require the repeated computation of conditional maxima of the likelihood function, which can cause computational difficulties especially in higher dimensions and when many signed root inversions are required, such as in the importance sampling...

.... The methods are confirmed through simulations and illustrated by application to real datasets on various sampling schemes including length-bias sampling, the case–cohort design and its variants. Accelerated failure time model Case–cohort design Counting process Estimating equations Importance sampling...

... a new method for inference using HIV deep sequencing data, using an approach based on importance sampling of ancestral recombination graphs under a multilocus coalescent model. The approach further extends recent progress in the approximation of so-called conditional sampling distributions...