MLE课程笔记

最新推荐文章于 2024-02-15 13:07:45 发布

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最新推荐文章于 2024-02-15 13:07:45 发布 · 843 阅读

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#机器学习

机器学习笔记

Random Variables 随机变量

Discrete Random Variables 离散随机变量

Bernoulli distribution伯努利（二项）分布

 X = {0, 1} 
 pmf p(x) = Bern(x|q), where 
 p(1) = Bern(1|q) = Pr[x = 1] = q (we also write p(x = 1)) 
 p(0) = Bern(0|q) = Pr[x = 0] = 1 1 q (we also write p(x = 0))

Categorical distribution 类别分布

X = {0, 1, ..., C-1}
pmf x ∼ p(x) = Cat(x|q), where
probability vector q = [q0, q1, ..., qCC 1] with
qk = q0 + q1 + ... + qCC 1 = 1
p(k) = Cat(k|q) = Pr[x = k] = qk (we also write p(x = k))

类别分布
one-hot vector
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Continuous Random Variables 连续随机变量

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Gaussian variable 高斯变量

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MATLAB 代码

	Description of any MATLAB function: help < namefunction >
	Generate a Bernoulli rv Bern(q): binornd(1, q) or (rand(1) < q)
	Generate N Bernoulli rvs Bern(q): binornd(1, q, N, 1) or (rand(N, 1) < q)
	Generate a categorical rv Cat(q): mnrnd(1, q)
	Generate N categorical rvs Cat(q): mnrnd(1, q, N) (produces one-hot vectors as rows)
	Generate a Gaussian rv N (m,s2): normrnd(m, s) or (m + s ∗ randn(1))
	Gaussian pdf N (x|m,s2): normpdf(x, m, s)
	Plot a function y = f (x): plot(x, y)