Search Results - "Metropolis–Hastings algorithm"

Bayesian inference for smooth transition autoregressive (STAR) model: A prior sensitivity analysis by Livingston, G., Nur, Darfiana

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Slice sampler algorithm for generalized pareto distribution by Rostami, Mohammad, Adam, Mohd Bakri, Yahya, Mohamed Hisham, Ibrahim, Noor Akma

“…The results were compared with another commonly used Markov chain Monte Carlo (MCMC) technique called Metropolis-Hastings algorithm. Based on the results, the slice sampler algorithm provides closer posterior mean values and shorter 95% quantile based credible intervals compared to the Metropolis-Hastings algorithm. …”
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Estimation of the Epidemiological Parameter for the COVID-19 Outbreak by Muhammad Fahmi, Ahmad Zuber, Norhayati, Rosli, Noryanti, Muhammad

“…In this study, we propose a Metropolis-Hastings algorithm of the Markov Chain Monte Carlo (MCMC) method to estimate the epidemiological parameters of infectious rate, fatality rate, recovery rate, and reproduction numbers. …”
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Slice sampler and metropolis hastings approaches for bayesian analysis of extreme data by Rostami, Mohammad

“…In this research, application of extreme value theory within a Bayesian framework using the Metropolis Hastings algorithm and the slice sampler algorithm as an alternative approach, has been introduced. …”
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A Bayesian approach for parameter estimation in multi-stage models by Pham, Hoa, Nur, Darfiana, Pham, Huong TT, Branford, Alan

“…In particular, a Metropolis-Hastings algorithm based on deterministic transformations is used to estimate parameters. …”
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Markov chain Monte Carlo convergence diagnostics for Gumbel model by Mohd Amin, Nor Azrita, Adam, Mohd. Bakri

“…The MCMC technique, Metropolis-Hastings algorithm is used for posterior inferences of Gumbel distribution simulated data.…”
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Multiple-try Metropolis Hastings for modeling extreme PM10 data by Mohd Amin, Nor Azrita, Adam, Mohd Bakri, Ibrahim, Noor Akma

“…The parameters were estimated using the new Bayesian approach in extreme called Multiple Try Metropolis-Hastings algorithms. We compared this approach with another Markov Chain Monte Carlo approach which is the classical Metropolis-Hastings algorithm and the frequentist approach, Maximum Likelihood Estimation. …”
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Mathematical modelling of bacterial mercury resistance by Crossland, Richard J.

“…These experiments were repeated in the computer simulation and the information from their 489 data points was incorporated into the 16 parameters of the model using the Metropolis-Hastings algorithm. This model is very useful biology for four reasons. …”
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A rare event approach to high-dimensional approximate Bayesian computation by Prangle, Dennis, Everitt, Richard G., Kypraios, Theodore

“…We use our rare event probability estimator as a likelihood estimate within the pseudo-marginal Metropolis-Hastings algorithm for parameter inference. We provide asymptotics showing that RE-ABC has a lower computational cost for high dimensional data than standard ABC methods. …”
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Bayesian survival and hazard estimates for Weibull regression with censored data using modified Jeffreys prior by Ahmed, Al Omari Mohammed

“…For the Weibull model with right censoring and unknown shape, the full conditional distribution for the scale and shape parameters are obtained via Gibbs sampling and Metropolis-Hastings algorithm from which the survival function and hazard function are estimated. …”
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Analyses of prior selections for Gumbel distribution by Rostami, Mohammad, Adam, Mohd Bakri

“…The usage of Markov Chain Monte Carlo via Metropolis-Hasting algorithm is implemented. Our findings show that the combination of Gumbel and Rayleigh are the most compromise pair of priors for Gumbel model. …”
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Epidemiological parameter estimation of sird model for covid-19 outbreak by Muhammad Fahmi, Ahmad Zuber, Norhayati, Rosli, Noryanti, Muhammad

“…This paper is devoted to the parameter estimation of the SIRD model using the Markov Chain Monte Carlo (MCMC) method of the Metropolis Hasting algorithm. The data from Malaysia, Thailand, and Indonesia are used and the dynamic behavior of the COVID-19 outbreak in these three countries is simulated. …”
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Stochastic modelling and Bayesian inference for the effect of antimicrobial treatments on transmission and carriage of nosocomial pathogens by Verykouki, Eleni

“…Results are obtained using Gaussian random walk Metropolis- Hastings algorithms. We find some evidence that decolonisation treatment and Oxazolidinone have a positive effect in clearing MRSA carriage. …”
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