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chain rule (probability), law of conditional probability
law of conditional probability
Conditional Probability the conditional probability that \mathrm{y}=y given \mathrm{x}=x as P(\mathrm{y}=y\mid\mathrm{x}=x) 반드시 P(\mathrm{x}=x)>0 인 상황에서만 성립이 된다.
Joint Likelihood Let there be a generative model m describing measured data y using model parameters θ and a prior distribution on θ .
Rule of Probabilities 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 \displaystyle p(Y\mid X)=\frac{p(X\mid...
Independence (probability) 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...
Chain Rule (probability) Related joint entropy, independence (probability), Bayes theorem Statistic Consider an indexed collection of random variables X {1},\ldots,X {n}.
Law of Total Probability B) 정리 (다른 이름으로는 marginalization) B.1) 이산형 경우 P(A)=\sum {n}P(A,B)=\sum {n}P\left(A\mid B {n}\right)P\left(B {n}\right) B.2) 연속형 경우 \displaystyle...
Probability Distribution Discrete 확률 분포 we use discrete probability distributions to model categorical variables.
Categorical Distribution Related References.
Joint Distribution 많은 변수에 대한 확률 분포를 joint probability distribution 이라고 한다.
Marginal Distribution 어떤 변수들의 집합에 대한 확률 분포에서, 부분 집합에 대한 확률 분포를 marginal probability distribution 이라고 한다. 2.