排序方式： 共有28条查询结果，搜索用时 18 毫秒
1.
Yalchin Efendiev Akhil Datta-Gupta Xianlin Ma Bani Mallick 《Mathematical Geosciences》2008,40(2):213-232
In this paper, the Markov Chain Monte Carlo (MCMC) approach is used for sampling of the permeability field conditioned on
the dynamic data. The novelty of the approach consists of using an approximation of the dynamic data based on streamline computations.
The simulations using the streamline approach allows us to obtain analytical approximations in the small neighborhood of the
previously computed dynamic data. Using this approximation, we employ a two-stage MCMC approach. In the first stage, the approximation
of the dynamic data is used to modify the instrumental proposal distribution. The obtained chain correctly samples from the
posterior distribution; the modified Markov chain converges to a steady state corresponding to the posterior distribution.
Moreover, this approximation increases the acceptance rate, and reduces the computational time required for MCMC sampling.
Numerical results are presented. 相似文献
2.
参数优化方法是准确估计生态模型参数、降低其不确定性的有效手段。本文提出一种基于贝叶斯机器学习的No-U-Turn Sampler（NUTS）生态模型参数优化方法。NUTS是一种高效的参数优化方法,每次取样中利用递归算法生成候选参数集（二叉树）推断参数的后验信息,如果满足约束条件“非U型回转”,不断构建子树更新参数;否则,记录本次抽样的“最优”参数集,并开始下一次取样,直到获取足够样本。该算法在每次取样中充分优化参数,避免因随机游走行为产生冗余抽样,提高了参数优化效率。本文以千烟洲亚热带人工针叶林碳通量模拟为例,基于Pymc3框架利用NUTS参数优化方法实现了碳通量（Net Ecosystem Exchange,NEE）模型参数反演,并与Metropolis-Hastings（MH）方法进行对比。结果表明,本文算法的参数值达到稳定波动时的抽样次数减少了85%左右,参数优化效率提升3倍左右。参数优化后,2种NEE模型中7个参数不确定性降低10%~53%。此外,NEE模拟效果明显提升,模拟值与实测值的R2分别提高23%和17%,RMSE分别降低3%和4%。综上所述,本文提出的参数优化方法对生态领域的参数估计或数据同化工作具有一定的借鉴意义。 相似文献
3.
This paper concerns efficient uncertainty quantification techniques in inverse problems for Richards’ equation which use coarse-scale simulation models. We consider the problem of determining saturated hydraulic conductivity fields conditioned to some integrated response. We use a stochastic parameterization of the saturated hydraulic conductivity and sample using Markov chain Monte Carlo methods (MCMC). The main advantage of the method presented in this paper is the use of multiscale methods within an MCMC method based on Langevin diffusion. Additionally, we discuss techniques to combine multiscale methods with stochastic solution techniques, specifically sparse grid collocation methods. We show that the proposed algorithms dramatically reduce the computational cost associated with traditional Langevin MCMC methods while providing similar sampling performance. 相似文献
4.
Trond Reitan Asgeir Petersen-Øverleir 《Stochastic Environmental Research and Risk Assessment (SERRA)》2009,23(5):627-642
This study explores Bayesian methods for handling compound stage–discharge relationships, a problem which arises in many natural
rivers. It is assumed: (1) the stage–discharge relationship in each rating curve segment is a power-law with a location parameter,
or zero-plane displacement; (2) the segment transitions are abrupt and continuous; and (3) multiplicative measurement errors
are of equal variance. The rating curve fitting procedure is then formulated as a piecewise regression problem where the number
of segments and the associated changepoints are assumed unknown. Procedures are developed for describing both global and site-specific
prior distributions for all rating curve parameters, including the changepoints. Estimation and uncertainty analysis is evaluated
using Markov chain Monte Carlo simulation (MCMC) techniques. The first model explored accounts for parameter and model uncertainties
in the interpolated area, i.e. within the range of available stage–discharge measurements. A second model is constructed in
an attempt to include the uncertainty in extrapolation, which is necessary when the rating curve is used to estimate discharges
beyond the highest or lowest measurement. This is done by assuming that the rate of changepoints both inside and outside the
measured area follows a Poisson process. The theory is applied to actual data from Norwegian gauging stations. The MCMC solutions
give results that appear sensible and useful for inferential purposes, though the latter model needs further efforts in order
to obtain a more efficient simulation scheme. 相似文献
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6.
多个粗差定位的抗掩盖型Bayes方法 总被引：3，自引：2，他引：1
在综合利用先验信息与观测信息的基础上,提出了多个粗差探测的Bayes方法。为了有效地防止掩盖和湮没现象的发生,在分析掩盖和湮没现象发生原因的基础上,从识别向量的样本相关系数阵的特征结构出发,提出了多个粗差定位的抗掩盖型Bayes方法,并设计了相应的算法——自适应MCMC抽样算法。 相似文献
7.
An assessment of coefficient accuracy in linear regression models with spatially varying coefficients 总被引：1，自引：3，他引：1
The realization in the statistical and geographical sciences that a relationship between an explanatory variable and a response
variable in a linear regression model is not always constant across a study area has led to the development of regression
models that allow for spatially varying coefficients. Two competing models of this type are geographically weighted regression
(GWR) and Bayesian regression models with spatially varying coefficient processes (SVCP). In the application of these spatially
varying coefficient models, marginal inference on the regression coefficient spatial processes is typically of primary interest.
In light of this fact, there is a need to assess the validity of such marginal inferences, since these inferences may be misleading
in the presence of explanatory variable collinearity. In this paper, we present the results of a simulation study designed
to evaluate the sensitivity of the spatially varying coefficients in the competing models to various levels of collinearity.
The simulation study results show that the Bayesian regression model produces more accurate inferences on the regression coefficients
than does GWR. In addition, the Bayesian regression model is overall fairly robust in terms of marginal coefficient inference
to moderate levels of collinearity, and degrades less substantially than GWR with strong collinearity. 相似文献
8.
Based upon the Bayesian framework for analyzing the discovery sequence in a play, a Markov chain Monte Carlo sampler—the Metropolis–Hastings algorithm, is employed to sample model parameters and pool sizes from their joint posterior distribution. The proposed sampling scheme ensures that the parameter space of changing dimension can be traversed in spite of the unknown number of pools. The equal sample weights make it easy to obtain the confidence intervals and assess the statistical error in the estimates, so that the statistical behaviors of the discovery process modeling can be well understood. Two application examples of the Halten play in Norwegian Sea and the Bashaw reef play in the Western Canada Basin show that, the computational advantage of this method to the simple Monte Carlo integration is considerable. In order to increase the convergence speed of the sample chains to the posterior distributions, several parallel simulations with different starting values are recommended. 相似文献
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10.
Grain-size distribution data,as a substitute for measuring hydraulic conductivity(K),has often been used to get K value indirectly.With grain-size distribution data of 150 sets of samples being input data,this study combined the Artificial Neural Network technology(ANN)and Markov Chain Monte Carlo method(MCMC),which replaced the Monte Carlo method(MC)of Generalized Likelihood Uncertainty Estimation(GLUE),to establish the GLUE-ANN model for hydraulic conductivity prediction and uncertainty analysis.By means of applying the GLUE-ANN model to a typical piedmont region and central region of North China Plain,and being compared with actually measured values of hydraulic conductivity,the relative error ranges are between 1.55%and 23.53%and between 14.08%and 27.22%respectively,the accuracy of which can meet the requirements of groundwater resources assessment.The global best parameter gained through posterior distribution test indicates that the GLUEANN model,which has satisfying sampling efficiency and optimization capability,is able to reasonably reflect the uncertainty of hydrogeological parameters.Furthermore,the influence of stochastic observation error(SOE)in grain-size analysis upon prediction of hydraulic conductivity was discussed,and it is believed that the influence can not be neglected. 相似文献