Statistique bayésienne, théorie de la décision et méthodes de simulation par chaînes de Markov

我想开发一种算法，当公司必须向客户提供新产品时，它将帮助公司。 该算法是一个贝叶斯过程给出预测概率的秩统计。 在模型中使用了Copula理论，并将构建马尔可夫链蒙特卡罗模拟进行数值评估。**接受-拒绝采样器是用于使用建议密度g从目标密度f采样的算法。 在该算法中，g生成的一些样本值在测试失败时将被拒绝。 当拒绝率较大时，采样较慢。 我想开发更好的采样器。 特别是，我建议使用马尔可夫链相关的接受拒绝采样器。 为了研究接受-拒绝采样器的现有变化，表明这种变化在比Caffo，Booth和Davison（2002）提出的条件更弱的条件下工作。在Copula理论中，我想回答Carley（2002）论文中提出的问题。 这些问题是关于子copula的copula扩展。在极值理论中，Copula由一个称为Pickands函数的函数来表征。 Pickands函数也与称为谱测度的东西有关。 在Guilleston和Perron（2016）中，我们已经描述了二维问题的所有多项式Pickands函数。 我想对三维问题中的谱测度做同样的处理。 我想在一个三维问题中，为谱测度建立一个贝叶斯非参数估计. 这个问题与累积分布函数的估计有关。 解决方案将基于Polya树。**在Pham Gia，Turkkan和Marchand（2006）中，比率X_1/X_2的密度（其中（X_1，X_2）的分布是正态分布的混合）通过特殊函数（如1F 1函数）表示。 我想利用一种新的混合表示法。 在混合物中，权重遵循混合泊松分布。 混合泊松分布在金融数学中非常重要。

I want to develop an algorithm that is going to help the companies when they have to offer new products to the customers. The algorithm is a Bayesian procedure giving predictive probabilities on rank statistics. The theory of copula is used in the model and a Markov Chain Monte Carlo simulation is going to be constructed for the numerical evaluations.******The acceptance-rejection sampler is an algorithm for sampling from a target density f using a proposal density g. In this algorithm some of the sample values generated by g will be rejected when they fail a test. When the rejection rate is large the sampling is slow. I want to develop better samplers. In particular, I propose to use a Markov chain related to the acceptance-rejection sampler. In want to work on an existing variation of the acceptance-rejection sampler showing that this variation works under weaker conditions than the ones proposed in Caffo, Booth and Davison (2002).******In the theory of copula I want to answer the questions raised in Carley's (2002) paper. These questions are about copula extensions of a subcopula.******In the theory of extreme values the copula is characterized by a function called the Pickands function. The Pickands function is also related to something called the spectral measure. In Guillotte and Perron (2016) we have characterized all of the polynomial Pickands functions for a 2-dimensional problem. I want to do the same for the spectral measure in a 3-dimensional problem. I want to develop a Bayesian nonparametric estimator for the spectral measure in a 3-dimensional problem. This problem is related to the estimation of a cumulative distribution function. The solution will be based on Polya trees.******In Pham Gia, Turkkan and Marchand (2006) the density of the ration X_1/X_2 where the distribution of (X_1,X_2) is a mixture of normal distributions is expressed through special functions like the 1F1 function. I want to exploit a new representation involving a mixture. In the mixture the weights follow a mixed Poisson distribution. The mixed Poisson distributions are very important in mathematical finance.*****