The use of the Bayesian tools in system identification and model updating paradigms has been increased in the last ten years. Usually, the Bayesian techniques can be implemented to incorporate the uncertainties associated with measurements as well as the prediction made by the finite element model (FEM) into the FEM updating procedure. In this case, the posterior distribution function describes...

We demonstrate the efficacy of a new spike-sorting method based on a Markov chain Monte Carlo (MCMC) algorithm by applying it to real data recorded from Purkinje cells (PCs) in young rat cerebellar slices. This algorithm is unique in its capability to estimate and make use of the firing statistics as well as the spike amplitude dynamics of the recorded neurons. PCs exhibit multiple discharge st...

1 Introduction Markov chain Monte Carlo (MCMC), which enables estimation in complex models via simulation, is now a widespread and accepted statistical tool, particularly in Bayesian analysis. Here, a distribution of interest, or target distribution, π, is approximated by a simulated chain {x

Sampling from the posterior distribution using Markov chain Monte Carlo (MCMC) methods can require an exhaustive number of iterations to fully explore the correct posterior. This is often the case when the posterior of interest is multi-modal, as the MCMC sampler can become trapped in a local mode for a large number of iterations. In this paper, we introduce the pseudo-extended MCMC method as a...

We derive a new upper bound on the tensor-to-scalar ratio parameter $r$ using frequentist profile likelihood method. vary all relevant cosmological parameters of $\Lambda$CDM model, as well nuisance parameters. Unlike Bayesian analysis Markov Chain Monte Carlo (MCMC), our is independent choice priors. Using $Planck$ Public Release 4, BICEP/Keck Array 2018, CMB lensing, and BAO data, we find an ...

In this study, the maximum likelihood estimation (MLE) and Bayes estimation are exploited to make interval estimation based on adaptive progressive TypeII censoring for the Burr Type-XII distribution. Explicit form for the parameters of Bayes estimator doesn’t exist, so, Markov Chain Monte Carlo (MCMC) method is used as approximation to find posterior mean under squared error loss function. Rea...

Objetivo: Proponer un criterio para determinar el tamaño de muestra en simulaciones estocásticas MC (Monte Carlo) y MCMC (Markov chain Monte Carlo), garantizando una determinada precisión la estimación parámetros. Se busca que se garantice forma adimensional. Materiales métodos: El presente artículo propone buscando cumplir con objetivo planteado. Además, metodología aplicación del mismo. Resul...

In this paper we propose an efficient Markov chain Monte Carlo (MCMC) method for estimation of discrete distributions by solving an appropriate system of linear equations. We call the estimator the equation-solving estimator. Our numerical results show that the new estimator makes significant improvements over the conventional frequencyMCMCestimator in terms of accuracy of the estimates. The ne...

We introduce a novel algorithm of community detection that maintains dynamically a community structure of a large network that evolves with time. The algorithm maximizes the modularity index thanks to the construction of a randomized hierarchical clustering based on a Monte Carlo Markov Chain (MCMC) method. Interestingly, it could be seen as a dynamization of Louvain algorithm (see [1]) where t...

In this paper, we provide bounds on the asymptotic variance for a class of sequential Monte Carlo (SMC) samplers designed for approximating multimodal distributions. Such methods combine standard SMC methods and Markov chain Monte Carlo (MCMC) kernels. Our bounds improve upon previous results, and unlike some earlier work, they also apply in the case when the MCMC kernels can move between the m...