1. Detailed criteria for priming and tolerance in the Metropolis searching algorithm. 2. Two-stage Metropolis search for parameter sets that exhibit priming or tolerance. 3. Statistical method used to identify backbone motifs. 4. Motif density is more robust than frequency to variation in the topological cut-off. 5. 2D parameter correlations demonstrate how parameter compensation affects topolo...

In an experimental study of single enzyme reactions, it has been proposed that the rate constants of the enzymatic reactions fluctuate randomly, according to a given distribution. To quantify the uncertainty arising from random rate constants, it is necessary to investigate how one can simulate such a biochemical system. To do this, we will take the Gillespie’s stochastic simulation algorithm f...

Markov chain Monte Carlo (MCMC) methods have proven to be a very powerful tool for analyzing data of complex structures. However, their computer-intensive nature, which typically require a large number of iterations and a complete scan of the full dataset for each iteration, precludes their use for big data analysis. In this paper, we propose the so-called bootstrap Metropolis-Hastings (BMH) al...

In this paper, we study generalized stress-strength model for logistic distribution. The maximum likelihood estimator of quantity is obtained and then a confidence interval presented it. Bayesian bootstrap methods are also applied the recommended model. A Markov Chain Monte Carlo (MCMC) simulation assessing estimation performed via Metropolis-Hastings algorithm in each step Gibbs algorithm. An ...

Two distinct strands of research are developed: new methodology for inference on the Markov modulated Poisson process (MMPP), and new theory on optimal scaling for the random walk Metropolis (RWM). A novel technique is presented for simulating from the exact distribution of a continuous time Markov chain over an interval given the start and end states and the infinitesimal generator. This is us...

Abstract The efficient resolution of optimization problems is one the key issues in today’s industry. This task relies mainly on classical algorithms that present scalability and processing limitations. Quantum computing has emerged to challenge these types problems. In this paper, we focus Metropolis-Hastings quantum algorithm, which based walks. We use algorithm build a software tool called M...

We consider a Metropolis–Hastings method with proposal N(x,hG(x)?1), where x is the current state, and study its ergodicity properties. show that suitable choices of G(x) can change these properties compared to Random Walk Metropolis case N(x,h?), either for better or worse. find if variance allowed grow unboundedly in tails distribution then geometric be established when target algorithm has a...

Abstract. It is known that reversible Langevin diffusions in confining potentials converge to equilibrium exponentially fast. Adding a divergence free component to the drift of a Langevin diffusion accelerates its convergence to stationarity. However such a stochastic differential equation (SDE) is no longer reversible. In this paper, we analyze the optimal scaling of MCMC algorithms constructe...

This paper presents the R package AdMit which provides flexible functions to approximate a certain target distribution and it provides an efficient sample of random draws from it, given only a kernel of the target density function. The core algorithm consists of the function AdMit which fits an adaptive mixture of Student-t distributions to the density of interest via its kernel function. Then,...

Suppose data consist of a random sample from a distribution function FY , which is unknown, and that interest focuses on inferences on θ, a vector of quantiles of FY . When the likelihood function is not fully specified, a posterior density cannot be calculated and Bayesian inference is difficult. This article considers an approach which relies on a substitution likelihood characterized by a ve...