# 计算机代写|密码学与网络安全代写cryptography and network security代考|CS499

#### Doug I. Jones

Lorem ipsum dolor sit amet, cons the all tetur adiscing elit

couryes-lab™ 为您的留学生涯保驾护航 在代写密码学与网络安全cryptography and network security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。我们的专家在代写密码学与网络安全cryptography and network security代写方面经验极为丰富，各种代写密码学与网络安全cryptography and network security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
couryes™为您提供可以保分的包课服务

## 计算机代写|密码学与网络安全代写cryptography and network security代考|Relations Between the Entropies

It is possible to show, using Bayes rule for the conditional probability, that the joint entropy can be written in terms of the conditional entropy, in the following way:
\begin{aligned} &H(X, Y)=H(X \mid Y)+H(Y) \ &H(X, Y)=H(Y \mid X)+H(X) \end{aligned}

Shannon has shown the fundamental inequality
$$H(X) \geq H(X \mid Y),$$
whose demonstration is given in the following.
The logarithm concavity property can be used to demonstrate the inequality, $\ln x \leq x-1$, as follows:
\begin{aligned} H(X \mid Y) &-H(X)=\sum_Y \sum_X p(x, y) \log \frac{p(x)}{p(x \mid y)} \ & \leq \sum_Y \sum_X p(x, y)\left(\frac{p(x)}{p(x \mid y)}-1\right) \log e \end{aligned}
But, the right-hand side of the inequality is zero, as shown in the following:
\begin{aligned} \sum_Y \sum_X(p(x) \cdot p(y)-p(x, y)) \log e &=\sum_Y(p(y)-p(y)) \log e \ &=0 \end{aligned}
Therefore,
$$H(X) \geq H(X \mid Y)$$
In a similar manner, it can be shown that
$$H(Y) \geq H(Y \mid X)$$
The equality is attained if and only if $X$ and $Y$ are statistically independent.

## 计算机代写|密码学与网络安全代写cryptography and network security代考|Mutual Information

A measure of mutual information provided hy two symhols $\left(x_i, y_i\right)$ can he written as
\begin{aligned} I\left(x_i ; y_j\right) &=\log _2 p\left(x_i \mid y_j\right)-\log _2 p\left(x_i\right) \ &=\log _2 \frac{p\left(x_i \mid y_j\right)}{p\left(x_i\right)}=\log \frac{p\left(x_i, y_j\right)}{p\left(x_i\right) p\left(y_j\right)} . \end{aligned}
It can be noticed that the a priori information of symbol $x_i$ is contained in the marginal probability $p\left(x_i\right)$. The a posteriori probability that symbol $x_i$ has been transmitted, given that $y_i$ was received, is $p\left(x_i \mid y_i\right)$. Therefore, in an informal way, the information gain for the observed symbol equals the difference between the initial information, or uncertainty, and the final one.
The mutual information is continuous in $p\left(x_i \mid y_i\right)$, and also symmetric, or
$$I\left(x_i ; y_j\right)=I\left(y_j ; x_i\right),$$
which indicates that the information provided by $x_i$ about $y_i$ is the same provided by $y_i$ about $x_i$.
The function $I\left(x_i ; x_i\right)$ can be called the auto-information of $x_i$, or
$$I\left(x_i\right)=I\left(x_i ; x_i\right)=\log \frac{1}{p\left(x_i\right)}$$
because, for an observer of the source alphabet, the a priori knowledge of the situation is that $x_i$ will be transmitted with probability $p\left(x_i\right)$, and the $a$ posteriori knowledge is the certainty that $x_i$ transmitted.
In conclusion,
\begin{aligned} I\left(x_i ; y_j\right) & \leq I\left(x_i ; x_i\right)=I\left(x_i\right) \ I\left(x_i ; y_j\right) & \leq I\left(y_j ; y_j\right)=I\left(y_j\right) \end{aligned}
The statistical mean of the mutual information per pairs of symbols provides an interesting interpretation of the mutual information concept
$$I(X ; Y)=E\left[I\left(x_i ; y_j\right)\right]=\sum_i \sum_j p\left(x_i, y_j\right) I\left(x_i ; y_j\right),$$
which can be written as
$$I(X ; Y)=\sum_i \sum_j p\left(x_i, y_j\right) \log \frac{p\left(x_i \mid y_j\right)}{p\left(x_i\right)}$$

# 密码学与网络安全代考

## 计算机代写|密码学与网络安全代写cryptography and network security代考|熵之间的关系

. .

\begin{aligned} &H(X, Y)=H(X \mid Y)+H(Y) \ &H(X, Y)=H(Y \mid X)+H(X) \end{aligned}

Shannon证明了基本不平等
$$H(X) \geq H(X \mid Y),$$
，其论证如下。

\begin{aligned} H(X \mid Y) &-H(X)=\sum_Y \sum_X p(x, y) \log \frac{p(x)}{p(x \mid y)} \ & \leq \sum_Y \sum_X p(x, y)\left(\frac{p(x)}{p(x \mid y)}-1\right) \log e \end{aligned}

\begin{aligned} \sum_Y \sum_X(p(x) \cdot p(y)-p(x, y)) \log e &=\sum_Y(p(y)-p(y)) \log e \ &=0 \end{aligned}

$$H(X) \geq H(X \mid Y)$$

$$H(Y) \geq H(Y \mid X)$$

## 计算机代写|密码学与网络安全代写cryptography and network security代考|Mutual Information

. security

$$I\left(x_i ; y_j\right)=I\left(y_j ; x_i\right),$$
，表示由 $x_i$ 关于 $y_i$ 是否提供相同的 $y_i$ 关于 $x_i$.

$$I\left(x_i\right)=I\left(x_i ; x_i\right)=\log \frac{1}{p\left(x_i\right)}$$

\begin{aligned} I\left(x_i ; y_j\right) & \leq I\left(x_i ; x_i\right)=I\left(x_i\right) \ I\left(x_i ; y_j\right) & \leq I\left(y_j ; y_j\right)=I\left(y_j\right) \end{aligned}每对符号互信息的统计平均值为互信息概念提供了一个有趣的解释
$$I(X ; Y)=E\left[I\left(x_i ; y_j\right)\right]=\sum_i \sum_j p\left(x_i, y_j\right) I\left(x_i ; y_j\right),$$

$$I(X ; Y)=\sum_i \sum_j p\left(x_i, y_j\right) \log \frac{p\left(x_i \mid y_j\right)}{p\left(x_i\right)}$$

## 有限元方法代写

tatistics-lab作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

## 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。

Days
Hours
Minutes
Seconds

# 15% OFF

## On All Tickets

Don’t hesitate and buy tickets today – All tickets are at a special price until 15.08.2021. Hope to see you there :)