统计代写|随机信号处理作业代写Statistical Signal Processing代考|Organization of the Book

statistics-lab™ 为您的留学生涯保驾护航 在代写随机信号处理Statistical Signal Processing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。我们的专家在代写随机信号处理Statistical Signal Processing代写方面经验极为丰富，各种代写随机信号处理Statistical Signal Processing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|随机信号处理作业代写Statistical Signal Processing代考|Organization of the Book

Chapter 2 provides a careful development of the fundamental concept of probability theory – a probability space or experiment. The notions of sample space, event space, and probability meastre are introduced, and several examples are toured. Independence and elementary conditional probability are developed in some detail. The ideas of signal processing and of random variables are introduced briefly as functions or operations on the output of an experiment. This in turn allows mention of the idea of expectation at an early stage as a generalization of the description of probabilities by sums or integrals.

Chapter 3 treats the theory of measurements made on experiments: random variables, which are scalar-valued measurements; random vectors, which are a vector or finite collection of measurements; and random processes, which can be viewed as sequences or waveforms of measurements. Random variables, vectors, and processes can all be viewed as forms of signal processing: each operates on “inputs,” which are the sample points of a probability space, and produces an “output,” which is the resulting sample value of the random variable, vector, or process. These output points together constitute an output sample space, which inherits its own probability measure from the structure of the measurement and the underlying experiment. As a result, many of the basic properties of random variables, vectors, and processes follow from those of probability spaces. Probability distributions are introduced along with probability mass functions, probability density functions, and cumulative distribution functions. The basic derived distribution method is described and demonstrated by example. A wide variety of examples of random variables, vectors, and processes are treated.

Chapter 4 develops in depth the ideas of expectation, averages of random objects with respect to probability distributions. Also called proba-bilistic averages, statistical averages, and ensemble averages, expectations can be thought of as providing simple but important parameters describing probability distributions. A variety of specific averages are considered, including mean, variance, characteristic functions, correlation, and covariance. Several examples of unconditional and conditional expectations and their properties and applications are provided. Perhaps the most important application is to the statement and proof of laws of large numbers or ergodic theorems, which relate long term sample average behavior of random processes to expectations. In this chapter laws of large numbers are proved for simple, but important, classes of random processes. Other important applications of expectation arise in performing and analyzing signal processing applications such as detecting, classifying, and estimating data. Minimum mean squared nonlinear and linear estimation of scalars and vectors is treated in some detail, showing the fundamental connections among conditional expectation, optimal estimation, and second order moments of random variables and vectors.

统计代写|随机信号处理作业代写Statistical Signal Processing代考|Probability

The theory of random processes is a branch of probability theory and probability theory is a special case of the branch of mathematics known as measure theory. Probability theory and measure theory both concentrate on functions that assign real numbers to certain sets in an abstract space according to certain rules. These set functions can be viewed as measures of the size or weight of the sets. For example, the precise notion of area in two-dimensional Euclidean space and volume in three-dimensional space are both examples of measures on sets. Other measures on sets in three dimensions are mass and weight. Observe that from elementary calculus we can find volume by integrating a constant over the set. From physics we can find mass by integrating a mass density or summing point masses over a set. In both cases the set is a region of three-dimensional space. In a similar manner, probabilities will be computed by integrals of densities of probability or sums of “point masses” of probability.

Both probability theory and measure theory consider only nonnegative real-valued set functions. The value assigned by the function to a set is called the probability or the measure of the set, respectively. The basic difference between probability theory and measure theory is that the former considers only set functions that are normalized in the sense of assigning the value of 1 to the entire abstract space, corresponding to the intuition that the abstract space contains every possible outcome of an experiment and hence should happen with certainty or probability 1. Subsets of the space have some uncertainty and hence have probability less than $1 .$

Probability theory begins with the concept of a probability space, which is a collection of three items.

统计代写|随机信号处理作业代写Statistical Signal Processing代考|A Uniform Spinning Pointer

Suppose that Nature (or perhaps Tyche, the Greek Goddess of chance) spins a pointer in a circle as depicted in Figure 2.1. When the pointer stops it can point to any number in the unit interval $[0,1) \triangleq{r: 0 \leq r<1}$. We call $[0,1)$ the sample space of our experiment and denote it by a capital Greek omega, $\Omega$. What can we say about the probabilities or chances of particular events or outcomes occurring as a result of this experiment? The sorts of events of interest are things like “the pointer points to a number between 0 and .5” (which one would expect should have probability $0.5$ if the wheel is indeed fair) or “the pointer does not lie between $0.75$ and $1^{“}$ (which should have a probability of $0.75$ ). Two assumptions are implicit here. The first is that an “outcome” of the experiment or an “event” to which we can assign a probability is simply a subset of $[0,1)$. The second assumption is that the probability of the pointer landing in any particular interval of the sample space is proportional to the length of the interval. This should seem reasonable if we indeed believe the spinning pointer to be “fair” in the sense of not favoring any outcomes over any others. The bigger a region of the circle, the more likely the pointer is to end up in that region. We can formalize this by stating that for any interval $[a, b]={r: a \leq r \leq b}$ with $0 \leq a \leq b<1$ we have that the probability of the event “the pointer lands

in the interval $[a, b]^{\top}$ is
$$P([a, b])=b-a$$

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。