Statistics 27
- Conditional Expectation and Sufficient Statistics in Measure Theoretic Aspects
- Paper Review: Necessary and Sufficient Statistics for a Family of Probability Distributions
- Normal Model, Noninformative Prior
- Informative Prior, the Conjugate
- Bayesian Approach(베이지안 접근)
- Weak Law for Random Functions(랜덤 함수와 랜덤 함수의 약한 큰 수의 법칙)
- Median as an Estimator(중앙값의 추정량으로써의 역할)
- Maximum Likelihood Estimator(최대가능도추정량)
- Several Types of Convergence(확률수렴, 분포수렴)
- Utility Theory - Construction of Utility(효용이론 - 효용함수의 정립)
- Utility Theory - Rewards and Utility(효용이론 - 보상과 효용)
- Bayesian Estimation(베이지안 추정)
- Proof of Factorization Theorem(분해정리의 증명)
- Conditional Distributions(조건부 분포)
- Product Measure(곱측도)
- Curved Exponential Families(곡선지수족)
- Cramer-Rao Bound and Fisher Information(크래머-라오 유계와 피셔 정보)
- Unbiased Estimators(불편추정량)
- The Rao-Blackwell Theorem(라오-블랙월 정리)
- Minimal Sufficiency and Completeness(최소충분성과 완비성)
- Factorization Theorem(분해 정리)
- Sufficiency(충분성)
- Generating Functions(생성함수)
- Exponential Families(지수족)
- Probability Densities(확률 밀도)
- Random Variables(확률 변수)
- What is Probability?(확률이란?)