chain rule (probability), law of conditional probability
law of conditional probability
the conditional probability that \mathrm{y}=y given \mathrm{x}=x as P(\mathrm{y}=y\mid\mathrm{x}=x)
Let there be a generative model m describing measured data y using model parameters θ and a prior distribution on θ .
chain rule sum rule: p(X)=\sum_{Y}p(X,Y) product rule: p(X,Y)=p(Y\mid X)p(X) relation with Bayes theorem
Related Marginal independence Conditional independence 정의 That is, knowledge of Y’s value doesn’t affect your belief in the value of X, given a value of Z. Variables A and B are...
P(A)=\sum_{n}P(A,B)=\sum_{n}P\left(A\mid B_{n}\right)P\left(B_{n}\right)
Discrete 확률 분포 we use discrete probability distributions to model categorical variables. i.e., variables that take a finite set of unordered values. Discrete distributions are...
X 가 주어졌을 때 Y 의 conditional entropy 는 다음과 같이 계산된다
Related joint entropy, independence (probability), Bayes theorem Statistic
많은 변수에 대한 확률 분포를 joint probability distribution 이라고 한다.
어떤 변수들의 집합에 대한 확률 분포에서, 부분 집합에 대한 확률 분포를 marginal probability distribution 이라고 한다.