assignmentutor-lab™ 为您的留学生涯保驾护航 在代写贝叶斯分析Bayesian Analysis方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写贝叶斯分析Bayesian Analysis代写方面经验极为丰富，各种代写贝叶斯分析Bayesian Analysis相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础

## 统计代写|贝叶斯分析代写Bayesian Analysis代考|Conditioning ôn Discrètè Evidèncè

Now that we have a scheme for propagating evidence through a hybrid BN let’s look at a simple example involving a discrete single observation, from which we wish to derive a continuous function conditioned on this observation. This is the simplest form of propagation in a hybrid $\mathrm{BN}$.

Consider a bank that needs to predict the future costs of some loan based on the yearly interest rate charged by another bank to provide this loan. Typically, the bank would expect to be able to afford the loan should interest rates stay at a manageable level. However, should they go beyond some value, specified by the regulator as stressful (because it is unexpected or rare), there is a risk that the interest payment due may be unaffordable and the bank might default on the loan.
Here we have some simple parameters for the model:

• The capital sum, which we will treat as a constant, $\$ 100 \mathrm{M}$. • A variable percentage monthly interest rate,$X$, and the regulator specifies the stress interest rate threshold as any value above$4 \%$interest$(X>4)$. • The number of months of the loan, which we will treat as a constant, say 10. Then, assuming a single interest payment is made at the end of the 10-month loan period, the total interest payable$Y$is equal to$100\left(1+\frac{X}{100}\right)^{10}-100$. Let’s assume percentage interest rates follow a LogNormal distribution with mean value$0.05$, and a standard deviation of$0.5$(don’t worry about the choice of distribution for now; we have simply chosen one that has an interesting shape), so $$X \sim \log \operatorname{Normal}(\mu=.05, \sigma=0.5)$$ and this distribution is shown in Figure 10.19. Notice that the marginal distribution is highly skewed with a long tail of low probability, high interest rate values. ## 统计代写|贝叶斯分析代写Bayesian Analysis代考|Classical versus Bayesian Modeling Recall that the objective of the induction idiom is to learn the true value of some population parameters (typically the mean or variance statistics) from observations (i.e., samples). The learned values of these parameters can then be used to make future predictions about members of the same population. This is the basis of Bayesian statistical inference (or adaptive updating as it is often called by computer scientists). We can solve this problem approximately using dynamic discretization by assigning reasonable prior distributions to the parameters of interest and then, for each of the observations we have, create nodes in the BN that depend on these parameters. When we execute the model the algorithm then learns the posterior marginal distributions for the parameters conditioned on the observations we have. To illustrate the solution we use the education department example in the sidebar. Now we know that the average school success rate prediction of$66.4 \%$fails to take into account any variability in the data. Indeed, as a prediction it will almost always be wrong since actual values will seldom exactly match it. How then do we represent our uncertainty in this problem and how likely is it that a prospective student would actually attend a school with that successs rate? Let’s first look at how this would be solved using classical frequentist methods and then turn to Bayesian alternatives. The most accessible classical frequentist approach to this problem involves using the Normal distribution and taking the mean and variance and using these as the parameter estimates. Translating the percentages into proportions (i.e. using$0.88$instead of$88 \%$etc), this approach yields estimates for population mean and variance respectively of $$\widehat{\mu}p=\bar{p}=\frac{\sum{i=1}^n p_i}{n}=0.664$$ and $$\hat{\sigma}p^2=\frac{\sum{i=1}^n\left(p_t-\bar{p}\right)^2}{n-1}=0.0391$$ # 贝叶斯分析代考 ## 统计代写|贝叶斯分析代写贝叶斯分析代考|条件反射ôn Discrètè Evidèncè 现在我们已经有了一个通过混合BN传播证据的方案，让我们来看一个包含离散单观察的简单例子，从这个例子中我们希望得到一个以该观察为条件的连续函数。这是在混合$\mathrm{BN}$.中传播的最简单形式 假设一家银行需要根据另一家银行提供这项贷款的年利率来预测未来的贷款成本。通常情况下，如果利率保持在可管理的水平上，银行将期望能够偿还贷款。然而，如果利率超过监管机构指定的某个压力值(因为它是意外或罕见的)，就存在到期利息支付可能无法承担的风险，银行可能会违约。这里我们有一些模型的简单参数: • 资本总额，我们将其视为常数$\$100 \mathrm{M}$ .
• 可变百分比月利率$X$，监管机构将压力利率阈值指定为$4 \%$ interest $(X>4)$以上的任何值
• 贷款的月数，我们将其视为常数，例如10。然后，假设在10个月的贷款期结束时支付了一笔利息，则应付的总利息$Y$等于$100\left(1+\frac{X}{100}\right)^{10}-100$。

$$X \sim \log \operatorname{Normal}(\mu=.05, \sigma=0.5)$$
，这个分布如图10.19所示。注意，边际分布高度倾斜，长尾为低概率、高利率值

## 统计代写|贝叶斯分析代写贝叶斯分析代考|经典vs贝叶斯建模

.

$$\widehat{\mu}p=\bar{p}=\frac{\sum{i=1}^n p_i}{n}=0.664$$

$$\hat{\sigma}p^2=\frac{\sum{i=1}^n\left(p_t-\bar{p}\right)^2}{n-1}=0.0391$$ 的总体均值和方差的估计

## 有限元方法代写

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## MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。

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