### 统计代写|贝叶斯统计代写Bayesian statistics代考|Point referenced data sets used in the book

statistics-lab™ 为您的留学生涯保驾护航 在代写贝叶斯统计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统计代写方面经验极为丰富，各种代写贝叶斯统计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|贝叶斯统计代写beyesian statistics代考|Air pollution in the eastern US

This example is taken from Sahu and Bakar (2012b), where we consider modeling the daily maximum 8-hour average ozone concentration data obtained from 691 monitoring sites in the eastern US, as shown in Figure $1.3$. These pollution monitoring sites are made up of 646 urban and suburban monitoring sites known as the National Air Monitoring Stations/State and Local Air Monitoring Stations (NAMS/SLAMS) and 45 rural sites monitored by the Clean Air Status and Trends Network (CASTNET).

We analyze daily data for $T=153$ days in every year from May to September since this is the high ozone season in the US. We consider these data for the 10 year period from 1997 to 2006 that allows us to study trend in ozone concentration levels. Thus, we have a total of $1,057,230$ observations and among them approximately $10.44 \%$ are missing, which we assume to be at random, although there are some annual variation in this percentage of missingness.
The main purpose of the modeling exercise here is to assess compliance with respect to the primary ozone standard which states that the 3 -year rolling average of the annual 4 th highest daily maximum 8-hour average ozone concentration levels should not exceed $85 \mathrm{ppb}$, see e.g., Sahu et al. (2007). Figure $1.4$ plots the 4 th highest maximum and their 3 -year rolling averages with a superimposed horizontal line at 85 . As expected, the plot of the rolling averages is smoother than the plot of the annual 4th highest maximum values. The plots show that many sites are compliant with respect to the standard, but many others are not. In addition, the plot of the 3 -year rolling averages shows a very slow downward trend. Both the plots show the presence of a few outlier sites which are perhaps due to site-specific issues in air pollution, for example, due to natural disasters such as forest fires. This data set is analyzed in Section 8.3.

## 统计代写|贝叶斯统计代写beyesian statistics代考|Hubbard Brook precipitation data

Measuring total precipitation volume in aggregated space and time is important for many environmental and ecological reasons such as air and water quality, the spatio-temporal trends in risk of flood and drought, forestry management and town planning decisions.

The Hubbard Brook Ecosystem Study (HBES), located in New Hampshire, USA and established in 1955 , continuously observes many environmental outcome variables such as temperature, precipitation volume, nutrient volumes in water streams. HBES is based on the 8,000 -acre Hubbard Brook Experimental Forest (see e.g. https://hubbardbrook.org/) and is a valuable source of scientific information for policy makers, members of the public, students and scientists. Of-interest here is a spatio-temporal data set on weekly precipitation volumes collected from 22 rain-gauges from 1997 to $2015 .$

Taken from Hammond et al. (2017), this example studies long-term trends in chlorophyll (chl) levels in the ocean, which is a proxy measure for phytoplankton (marine algae). Phytoplankton is at the bottom of food chain and provides the foundation of all marine ecosystem. The abundance of phytoplankton affects the supply of nutrients and light exposure. Global warming can potentially affect the phytoplankton distribution and abundance, and hence it is of much scientific interest to study long-term trends in chl which influences the abundance of phytoplankton.

Figure $1.6$ shows a map of the 23 ocean regions of interest where we have observed satellite-based measurements. The main modeling objective here is to study long-term trends in chl levels in these 23 oceanic regions. Section $8.5$ assesses these trend values.

## 统计代写|贝叶斯统计代写beyesian statistics代考|Atlantic ocean temperature and salinity data set

This example is taken from Sahu and Challenor (2008) on modeling deep ocean temperature data from roaming Argo floats. The Argo float program, see for example, http://www.argo.ucsd.edu, is designed to measure the temperature and salinity of the upper two kilometers of the ocean globally. These floats record the actual measurements which are in contrast to satellite data, such as the ones used in the ocean chlorophyll example in Section 1.3.5, which provide less accurate observations with many missing observations. Each Argo float is programmed to sink to a depth of one kilometer, drifting at that depth for about 10 days. After this period the float sinks a further kilometer to a depth of two kilometers and adjusting its buoyancy rises to the surface, measuring temperature and conductivity (from which salinity measurements are derived) on the way. Once at the surface, the data and the position of the float are transmitted via a satellite. This gives scientists access to near realtime data. After transmitting the data the float sinks back to its ‘resting’ depth

of one kilometer and drifts for another ten days before measuring another temperature and salinity profile at a different location. Argo data are freely available via the international Argo project office, see the above-mentioned website.

We consider the data observed in the North Atlantic ocean between the latitudes $20^{\circ}$ and $60^{\circ}$ north and longitudes $10^{\circ}$ and $50^{\circ}$ west. Figure $1.7$ shows the locations of the Argo floats in the deep ocean. The figure shows the moving nature of Argo floats in each of the 12 months. The primary modeling objective here is to construct an annual map of temperature at the deep ocean along with its uncertainty. The time points at which the data are observed are not equi-lagged, and we do not assume this in our modeling endeavor. Modeling required to produce an annual temperature map of the North Atlantic ocean is performed in Section 8.6.

## 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。