数据科学基础|DATA1001/ COMP615/CS910/MATHS 7027 Foundations of Data Science代写

0

这是一份贝叶斯统计推断作业代写的成功案

数据科学基础|DATA1001/ COMP615/CS910/MATHS 7027 Foundations of Data Science代写



Keep in mind that the leading terms here for large $k$ are the last two and, in fact, at $k=n$, they cancel each other so that our argument does not prove the fallacious statement for $c \geq 1$ that there is no connected component of size $n$, since there is. Let
$$
f(k)=\ln n+k+k \ln \ln n-2 \ln k+k \ln c-c k \ln n+c k^{2} \frac{\ln n}{n}
$$
Differentiating with respect to $k$,
$$
f^{\prime}(k)=1+\ln \ln n-\frac{2}{k}+\ln c-c \ln n+\frac{2 c k \ln n}{n}
$$
and
$$
f^{\prime \prime}(k)=\frac{2}{k^{2}}+\frac{2 c \ln n}{n}>0 .
$$
Thus, the function $f(k)$ attains its maximum over the range $[2, n / 2]$ at one of the extreme points 2 or $n / 2$. At $k=2, f(2) \approx(1-2 c) \ln n$ and at $k=n / 2, f(n / 2) \approx-c \frac{n}{4} \ln n$. So



英国论文代写Viking Essay为您提供实分析作业代写Real anlysis代考服务

DATA1001/ COMP615/CS910/MATHS 7027 COURSE NOTES :

$$
\sum_{i=0}^{\infty} \frac{i}{i !}=\sum_{i=1}^{\infty} \frac{i}{i !}=\sum_{i=1}^{\infty} \frac{1}{(i-1) !}=\sum_{i=0}^{\infty} \frac{1}{i !}
$$
and
$$
\sum_{i=0}^{\infty} \frac{i^{2}}{i !}=\sum_{i=1}^{\infty} \frac{i}{(i-1) !}=\sum_{i=0}^{\infty} \frac{i+1}{i !}=\sum_{i=0}^{\infty} \frac{i}{i !}+\sum_{i=0}^{\infty} \frac{1}{i !}=2 \sum_{i=0}^{\infty} \frac{1}{i !}
$$
Thus,
$$
\sum_{i=0}^{\infty} \frac{i(i-2)}{i !}=\sum_{i=0}^{\infty} \frac{i^{2}}{i !}-2 \sum_{i=0}^{\infty} \frac{i}{i !}=0
$$




数据科学基础|Foundations of Data Science 代写 COMP6235

0

这是一份southampton南安普顿大学 COMP6235作业代写的成功案

数据科学基础|Foundations of Data Science代写 COMP6235
问题 1.

If $\mathbf{x}$ and $\mathbf{y}$ are two independent samples from the same spherical Gaussian with standard deviation ${ }^{1} \sigma$, then
$$
|\mathbf{x}-\mathbf{y}|^{2} \approx 2(\sqrt{d} \pm c)^{2} \sigma^{2}
$$


证明 .

  • If $\mathbf{x}$ and $\mathbf{y}$ are samples from different spherical Gaussians each of standard deviation $\sigma$ and means separated by distance $\delta$, then
    $$
    |\mathbf{x}-\mathbf{y}|^{2} \approx 2(\sqrt{d} \pm c)^{2} \sigma^{2}+\delta^{2} .
    $$

英国论文代写Viking Essay为您提供实分析作业代写Real anlysis代考服务

PHAS0038 COURSE NOTES :

\begin{aligned}
B=A A^{T} &=\left(\sum_{i} \sigma_{i} \mathbf{u}{\mathbf{i}} \mathbf{v}{\mathbf{i}}^{T}\right)\left(\sum_{j} \sigma_{j} \mathbf{u}{\mathbf{j}} v{j}^{T}\right)^{T} \
&=\sum_{i} \sum_{j} \sigma_{i} \sigma_{j} \mathbf{u}{\mathbf{i}} \mathbf{v}{\mathbf{i}}^{T} \mathbf{v}{\mathbf{j}} \mathbf{u}{\mathbf{j}}^{T} \
&=\sum_{i} \sigma_{i}^{2} \mathbf{u}{\mathbf{i}} \mathbf{u}{\mathbf{i}}^{T}
\end{aligned}