نتایج جستجو برای: روش mcmc
تعداد نتایج: 374284 فیلتر نتایج به سال:
Markov Chain Monte Carlo (MCMC) is a family of algorithms which is used to draw samples from arbitrary probability distributions in order to estimate otherwise intractable integrals. When the distribution is complex, simple MCMC becomes inefficient and advanced variations are employed. This paper proposes a novel FPGA architecture to accelerate Parallel Tempering, a computationally expensive, p...
هدف: این پژوهش با هدف بررسی اثر آموزش برنامه هوش موفق استرنبرگ بر افزایش خلاقیت و تحمل ابهام دانشآموزان انجام شد. روش: روش آزمایشی طرح پیش آزمون پس پیگیری گروه کنترل بود. جامعۀ آماری را 15842 نفر ازدانشآموزان پسر کلاس چهارم در سال تحصیلی1399-1400 شهر اصفهان تشکیل دادند که30 نمونهگیری تصادفی چند مرحلهای انتخاب به صورت دو آزمایش (15 نفر) گمارده شدند. ده جلسه برنامهی دریافت نمود. داده...
هدف: این پژوهش با هدف آزمون مدل روابط بین سبکهای مقابله و خودکارآمدی حرفهای استرس ادراک شده ناشی از حرفههای سلامت در گروهی پرستاران انجام شد. روش: همبستگی، 151 نفر شاغل بخشهای اونکولوژی دانشگاه علوم پزشکی تهران به پرسشنامه باورهای شغلی برای (پیسانتی، لامباردو، لوسیدی، لازاری برتینی، 2008)، نسخة کوتاه سیاهه موقعیتهای استرسزا (کوهن، جانگ استین، 2006) (ولفگانگ، 1988) پاسخ دادند. مفروض روش آم...
Markov chain Monte Carlo (MCMC) methods asymptotically sample from complex probability distributions. The pseudo-marginal MCMC framework only requires an unbiased estimator of the unnormalized probability distribution function to construct a Markov chain. However, the resulting chains are harder to tune to a target distribution than conventional MCMC, and the types of updates available are limi...
Bayesian computation crucially relies on Markov chain Monte Carlo (MCMC) algorithms. In the case of massive data sets, running the Metropolis-Hastings sampler to draw from the posterior distribution becomes prohibitive due to the large number of likelihood terms that need to be calculated at each iteration. In order to perform Bayesian inference for a large set of time series, we consider an al...
We here propose an exponential family of permutation models that is suitable for inferring the direction and strength of association among dyadic relational structures. A linear-time algorithm is shown for MCMC simulation of model draws, as is the use of simulated draws for maximum likelihood estimation (MCMC-MLE) and/or estimation of Monte Carlo standard errors. We also provide an easily perfo...
In many modern applications, difficulty in evaluating the posterior density makes performing even a single MCMC step slow. This difficulty can be caused by intractable likelihood functions, but also appears for routine problems with large data sets. Many researchers have responded by running approximate versions of MCMC algorithms. In this note, we develop quantitative bounds for showing the er...
We propose a general MCMC method for Bayesian inference in logic-based probabilistic modeling. It covers a broad class of generative models including Bayesian networks and PCFGs. The idea is to generalize an MCMC method for PCFGs to the one for a Turing-complete probabilistic modeling language PRISM in the context of statistical abduction where parse trees are replaced with explanations. We des...
In this paper we propose a Bayesian method for estimating hyperbolic diffusion models. The approach is based on the Markov Chain Monte Carlo (MCMC) method after discretization via the Milstein scheme. Our simulation study shows that the hyperbolic diffusion exhibits many of the stylized facts about asset returns documented in the financial econometrics literature, such as a slowly declining aut...
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