### 统计代写|贝叶斯分析代写Bayesian Analysis代考|Introduction to the Bayesian Analysis of Time Series

statistics-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|Bayesian Inference and MCMC Simulation

The chapter begins with a description of Bayes theorem followed by the four phases of statistical inference, namely: (1) prior information, (2) sample information represented by the likelihood function, (3) the joint posterior distribution of the parameters, and (4) if appropriate, forecasting future observations via the Bayesian predictive density (or mass function). Bayesian inferential techniques are illustrated with many examples. For example, the first population to be considered is the binomial with one parameter, the probability of success in $n$ independent trials, where a beta prior is placed on the parameter, which when combined with the likelihood function gives a beta posterior distribution for the probability of success. Based on the posterior distribution of the parameter, point estimation, and interval estimation are described, and finally the Bayesian predictive mass function is derived for $\mathrm{m}$ future independent trials, and demonstrations for the binomial population is done with $n=11$ trials and $m=5$ future trials. The same four-phase scenario is explained for the normal, multinomial, and Poisson populations.

The remaining sections of the chapter are devoted to the description of MCMC algorithms for the simulation of observations from the relevant posterior distributions. Also included in the concluding sections of the chapter are detailed directions for how to use $\mathrm{R}$ and WinBUGS.

## 统计代写|贝叶斯分析代写Bayesian Analysis代考|Fundamentals of Time Series Analysis

A time series is defined as a stochastic process whose index is time, and several examples are explained:
(1) Airline passenger bookings over a 10-year period, (2) the sunspot cycle, and (3). Annual Los Angeles rainfall data, starting in 1903. The first two series have an increasing trend over time and clearly have seasonal components, while the third (the rainfall data) has a constant trend and no obvious seasonal effects. At this point, the student is shown how to employ $R$ to graph the data and how to delineate the series into trend and seasonal components. The $\mathrm{R}$ package has a function called decomposition that portrays the observations, the trend of the series, the seasonal component, and the errors of a given series. Decomposition is a powerful tool and should always be used when analyzing a time series. This will give the investigator enough information to propose a tentative time series model. Also presented are definitions of the mean value function and variance function of the time series, the autocorrelation function, and the correlogram. These characteristic of a time series are computed for the airline passenger booking data, the sunspot cycle data, and the Los Angeles rainfall data using the appropriate $R$ command (e.g. mean(), std () , autocorrelation or $\operatorname{acf}()$, and partial autocorrelation or pacf()).

## 统计代写|贝叶斯分析代写Bayesian Analysis代考|Basic Random Models

This chapter describes the building blocks for constructing more complex time series models, beginning with white noise, a sequence of independent normally identically distributed random variables. This sequence will serve as the error terms for the autoregressive time series models. We now progress from white noise to the random walk time series, and random walk with drift. For the autoregressive time series, the likelihood function is explicated, a prior distribution for the parameters is specified, and via Bayes theorem, the posterior distribution of the autoregressive parameters and precision of the error terms are derived. $\mathrm{R}$ is employed to generate observations from an AR(1) model with known parameters; then using those observations as data, WinBUGS is executed for the posterior analysis which gives Bayesian point and interval estimates of the unknown parameters. Since the parameter values used to generate the data are known, the posterior means and medians can be compared to them giving one some idea of how ‘close’ these estimates are to the so-called ‘true’ values. Another example of an autoregressive time series is presented, followed by a Bayesian approach to goodness-of-fit, and finally the Bayesian predictive density for a future observation is derived.

## 统计代写|贝叶斯分析代写Bayesian Analysis代考|Fundamentals of Time Series Analysis

（1）10年期间的航空公司乘客预订，（2）太阳黑子周期，以及（3）。洛杉矶的年降雨量数据，从 1903 年开始。前两个系列随时间推移呈增加趋势，并且明显具有季节性成分，而第三个系列（降雨量数据）具有恒定趋势，没有明显的季节性影响。在这一点上，向学生展示了如何使用R将数据绘制成图表，以及如何将系列描绘成趋势和季节性成分。这R包有一个称为分解的函数，它描述了观测值、序列的趋势、季节性分量和给定序列的误差。分解是一种强大的工具，在分析时间序列时应始终使用。这将为调查人员提供足够的信息来提出暂定的时间序列模型。还介绍了时间序列的均值函数和方差函数、自相关函数和相关图的定义。使用适当的方法为航空公司乘客预订数据、太阳黑子周期数据和洛杉矶降雨数据计算时间序列的这些特征R命令（例如 mean()、std () 、自相关或acf⁡(), 和偏自相关或 pacf())。

## 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。