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Showing 4 results for McMc
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Volume 14, Issue 2 (3-2010)
Abstract
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Volume 22, Issue 1 (12-2017)
Abstract
Analysis of large geostatistical data sets, usually, entail the expensive matrix computations. This problem creates challenges in implementing statistical inferences of traditional Bayesian models. In addition,researchers often face with multiple spatial data sets with complex spatial dependence structures that their analysis is difficult. This is a problem for MCMC sampling algorithms that are commonly used in Bayesian analysis of spatial models, causing serious problems such as slowing down and chain integration. To escape from such computational problems, we use low-rank models, to analyze Gaussian geostatistical data. This models improve MCMC sampler convergence rate and decrease sampler run-time by reducing parameter space. The idea here is to assume, quite reasonably, that the spatial information available from the entire set of observed locations can be summarized in terms of a smaller, but representative, sets of locations, or ‘knots’. That is, we still use all of the data but we represent the spatial structure through a dimension reduction. So, again, in implementing the reduction, we need to design the knots. Consideration of this issue forms the balance of the article. To evaluate the performance of this class of models, we conduct a simulation study as well as analysis of a real data set regarding the quality of underground mineral water of a large area in Golestan province, Iran.
Ali Reza Taheriyoun, Gazelle Azadi,
Volume 26, Issue 1 (12-2021)
Abstract
Profile monitoring is usually faced by control charts and mostly the response variable is observable in those problems. We confront here with a similar problem where the values of the reward function are observed instead of the response variable vector and we use the dart model to make it easier to understand. Supposing there exists at most one change-point, a sequence of independent points resulted by darts throws is observed and the estimation of parameters and the change-point (if there exists any) are presented using the frequentist and Bayesian approaches. In both the approaches, two possible precision scalar and matrix are studied separately. The results are examined through a simulation study and the methods applied on a real data.
Dr. Nahid Sanjari Farsipour, Dr. Bahram Tarami, Mrs Zahra Memar Kashani,
Volume 28, Issue 2 (3-2024)
Abstract
Marshall-Olkin introduced a family of distributions which obtained by adding a parameter into other distributions. Santoz-Neto etal study an extended Weibull distribution. In this paper two Raiyle and Pareto extended weibull are studied under some momemts and Bayesian methods with some loss functions such as squared error, entropy, linex, squared error in logarithm and modified linex. Also the MCMC method are study for these two distributions.
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