We study inference for parameters defined by either classical extremum estimators or Laplace-type subject to general nonlinear constraints on the parameters. show that running MCMC penalized version of problem offers a computationally attractive alternative solving original constrained optimization problem. Bayesian credible intervals are asymptotically valid confidence in pointwise sense, prov...

Abstract Efficient sampling of many-dimensional and multimodal density functions is a task great interest in many research fields. We describe an algorithm that allows parallelizing inherently serial Markov chain Monte Carlo (MCMC) by partitioning the space function parameters into multiple subspaces each them independently. The samples different are then reweighted their integral values stitch...

هدف: هدف از این پژوهش بررسی واسطه‌­ای راهبردهای سازش نایافته تنظیم شناختی هیجان در رابطه باورهای فراشناختی با اضطراب امتحان بود. روش: روش حاضر توصیفی و نوع همبستگی جامعه آماری را کلیه دانش‌­آموزان پسر دوره دوم متوسطه شهر تهران سال تحصیلی 1401-1400 تشکیل دادند که تعداد 456 نفر به نمونه‏‌گیری دسترس انتخاب شرکت کردند. ابزارها شامل سیاهه ابوالقاسمی همکاران (1375)، پرسشنامه باکو دیگران (2009)، فرم ک...

Markov chain Monte Carlo (MCMC) methods have not been broadly adopted in Bayesian neural networks (BNNs). This paper initially reviews the main challenges sampling from parameter posterior of a network via MCMC. Such culminate to lack convergence posterior. Nevertheless, this shows that nonconverged chain, generated MCMC space network, can yield marginalization valuable predictive distribution ...

In geostatistics, Gaussian random fields are often used to model heterogeneities of soil or subsurface parameters. To give spatial approximations these fields, they discretized. Then, different techniques geostatistical inversion condition them on measurement data. Among techniques, Markov chain Monte Carlo (MCMC) stand out, because yield asymptotically unbiased conditional realizations. Howeve...

This paper presents the reliability analysis of three benchmark problems using three variants of Subset Simulation. The original version of Subset Simulation, SubSim/MCMC, employs a Markov chain Monte Carlo (MCMC) method to simulate samples conditional on intermediate failure events; it is a general method that is applicable to all the benchmark problems. SubSim/Splitting is a variant of Subset...

A Markov chain Monte Carlo (MCMC) method and a bootstrap method were compared in the estimation of standard errors of item response theory (IRT) true score equating. Three test form relationships were examined: parallel, tauequivalent, and congeneric. Data were simulated based on Reading Comprehension and Vocabulary tests of the Iowa Tests of Basic Skills1. For parallel and congeneric test form...

The evolution of DNA sequences can be described by discrete state continuous time Markov processes on a phylogenetic tree. We consider neighbor-dependent evolutionary models where the instantaneous rate of substitution at a site depends on the states of the neighboring sites. Neighbor-dependent substitution models are analytically intractable and must be analyzed using either approximate or sim...