Machine Learning formula in LaTex
Bayesian
P(A|B)=\frac{P(B|A)P(A)}{P(B)}
P(A|B)=\frac{P(B|A)P(A)}{P(B)}
posterior=\frac{likelihood\cdot prior}{constant}
Binomial distribution
P(X=k)=C_n^kp^k(1-p)^{n-k}\\[5pt]n\ is\ the\ total number\ of\ experiments\\ k\ is\ the\ number\ of\ successful\ times\\ p\ is\ the\ probability\ of\ success
Poisson distribution
Poisson distribution is to describe the specific occurrence probability of events in a certain period of time.
P(X=k)=\frac{\lambda^k}{k!}e^{-\lambda},k=0,1,…
P(X=k)=\frac{\lambda^k}{k!}e^{-\lambda},k=0,1,…\\\lambda\ is\ mean,\ k\ is\ times
Exponential distribution
Exponential distribution is a kind of continuous probability distribution, which is used to represent the time interval of independent random events.
f(n)= \begin{cases} \lambda e^{-\lambda x}\quad x>0\\ 0 \quad\quad\quad x\leq0\end{cases}
f(n)= \begin{cases} \lambda e^{-\lambda x}\quad x>0\\ 0 \quad\quad\quad x\leq0\end{cases}
Normal distribution
f(x)=\frac{1}{\sqrt{2\pi}\sigma}e^\frac{(x-\mu)^2}{2\sigma^2}
f(x)=\frac{1}{\sqrt{2\pi}\sigma}e^\frac{(x-\mu)^2}{2\sigma^2}\\\mu\ is\ mean,\ \sigma\ is\ standard\ deviation
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