<p>The estimated frequency is shown in the upper panel with the vertical dashed line representing the observed frequency. The associated Gelman-Rubin-Brooks plot demonstrating convergence during MCMC simulation is shown in the lower panel together with median (—) and 97.5% credible interval (----).</p
. Markov Chain Monte Carlo (MCMC) methods, as introduced by Gelfand and Smith (1990), provide a simu...
Most Markov chain Monte Carlo (MCMC) users address the convergence problem by applying diagnostic to...
<p>The traces of the MCMC chain (grey line) show that the chain has converged, indicating that there...
Results on the capability of convergence of the HDMM on simulated data similar to the onco-hematolog...
<p>(A) The trace of slope and intercept parameters is shown as a function of MCMC step. The traces f...
A critical issue for users of Markov Chain Monte Carlo (MCMC) methods in applications is how to dete...
AbstractMarkov chain Monte Carlo (MCMC) simulation methods are being used increasingly in statistica...
<p>MC Distribution is the sampling distribution of each parameter used in the Monte Carlo simulation...
Markov chain Monte Carlo (MCMC) is a sampling technique that allows for estimating features of intra...
The accessibility of Markov Chain Monte Carlo (MCMC) methods for statistical inference have improved...
Important in the application of Markov chain Monte Carlo (MCMC) methods is the determination that a ...
This paper is organised as follows. In Section 2, we present an over-simplified version of a converg...
Gelman and Rubin’s (Statist. Sci. 7 (1992) 457–472) convergence diagnostic is one of the most popula...
Top: Log-likelihood trace plot. 2nd-4th row: Posterior distributions for the spatial transmission ra...
Markov chain Monte Carlo (MCMC) has been widely used in Bayesian analysis for the analysis of comple...
. Markov Chain Monte Carlo (MCMC) methods, as introduced by Gelfand and Smith (1990), provide a simu...
Most Markov chain Monte Carlo (MCMC) users address the convergence problem by applying diagnostic to...
<p>The traces of the MCMC chain (grey line) show that the chain has converged, indicating that there...
Results on the capability of convergence of the HDMM on simulated data similar to the onco-hematolog...
<p>(A) The trace of slope and intercept parameters is shown as a function of MCMC step. The traces f...
A critical issue for users of Markov Chain Monte Carlo (MCMC) methods in applications is how to dete...
AbstractMarkov chain Monte Carlo (MCMC) simulation methods are being used increasingly in statistica...
<p>MC Distribution is the sampling distribution of each parameter used in the Monte Carlo simulation...
Markov chain Monte Carlo (MCMC) is a sampling technique that allows for estimating features of intra...
The accessibility of Markov Chain Monte Carlo (MCMC) methods for statistical inference have improved...
Important in the application of Markov chain Monte Carlo (MCMC) methods is the determination that a ...
This paper is organised as follows. In Section 2, we present an over-simplified version of a converg...
Gelman and Rubin’s (Statist. Sci. 7 (1992) 457–472) convergence diagnostic is one of the most popula...
Top: Log-likelihood trace plot. 2nd-4th row: Posterior distributions for the spatial transmission ra...
Markov chain Monte Carlo (MCMC) has been widely used in Bayesian analysis for the analysis of comple...
. Markov Chain Monte Carlo (MCMC) methods, as introduced by Gelfand and Smith (1990), provide a simu...
Most Markov chain Monte Carlo (MCMC) users address the convergence problem by applying diagnostic to...
<p>The traces of the MCMC chain (grey line) show that the chain has converged, indicating that there...