# ## 주메뉴

### 확률 및 통계학

• 건국대학교
• 이향원 • 주제분류
자연과학 >수학ㆍ물리ㆍ천문ㆍ지리 >통계학
• 강의학기
2014년 2학기
• 조회수
48,879
• 평점
5/5.0 (4)
확률 및 통계의 기본적인 무작위 변수, 확률 분포, 특수 분포 및 그들의 연관성, 평가법, 가설의 검증 및 희귀법 등에 관한 것을 습득하고 이들의 응용에 관한 것을 공부한다.
Introduction #### 차시별 강의      1. Introduction Introduction, Sets, probabilistic models, conditional probability, total probability theorem, Bayes'rule 2. Introduction Introduction, Sets, probabilistic models, conditional probability, total probability theorem, Bayes'rule 3. Introduction Introduction, Sets, probabilistic models, conditional probability, total probability theorem, Bayes'rule 4. Introduction/Independence and random variables Introduction, Sets, probabilistic models, conditional probability, total probability theorem, Bayes'rule/ Independence, counting, discrete random variables (basic concepts, PMFs and function of random variables) 5. Independence and random variables Independence, counting, discrete random variables (basic concepts, PMFs and function of random variables) 6. Independence and random variables Independence, counting, discrete random variables (basic concepts, PMFs and function of random variables) 7. Independence and random variables Independence, counting, discrete random variables (basic concepts, PMFs and function of random variables) 9. Discrete random variables Expectation, mean and bariance, hoint PMFs of multiple RVs, conditioning, independence 10. Discrete random variables Expectation, mean and bariance, hoint PMFs of multiple RVs, conditioning, independence 11. Continuous random variables PDFs, CDFs, Nomal random variables, joint PDFs of multiple random variables 12. Continuous random variables PDFs, CDFs, Nomal random variables, joint PDFs of multiple random variables 13. Continuous random variables PDFs, CDFs, Nomal random variables, joint PDFs of multiple random variables 14. Continuous random variables PDFs, CDFs, Nomal random variables, joint PDFs of multiple random variables 15. Continuous random variables PDFs, CDFs, Nomal random variables, joint PDFs of multiple random variables  Continuous random variables PDFs, CDFs, Nomal random variables, joint PDFs of multiple random variables  Continuous random variables PDFs, CDFs, Nomal random variables, joint PDFs of multiple random variables  Continuous random variables PDFs, CDFs, Nomal random variables, joint PDFs of multiple random variables  Continuous random variables PDFs, CDFs, Nomal random variables, joint PDFs of multiple random variables  Continuous random variables PDFs, CDFs, Nomal random variables, joint PDFs of multiple random variables  Continuous random variables PDFs, CDFs, Nomal random variables, joint PDFs of multiple random variables  Conditioning and bayes rule Conditioning, continuous Bayes rule, derived distributions  Conditioning and bayes rule Conditioning, continuous Bayes rule, derived distributions  Covariance and transforms Covariance, correlation, conditional expectation, transforms  Covariance and transforms Covariance, correlation, conditional expectation, transforms  Covariance and transforms Covariance, correlation, conditional expectation, transforms  Bayesian inference Bayesian inference, posterior distribution  Estimation Point estimation, hypothesis testion, MAP rule, Bayesian least mean squares estimation  Estimation Point estimation, hypothesis testion, MAP rule, Bayesian least mean squares estimation #### 연관 자료 #### 사용자 의견

강의 평가를 위해서는 로그인 해주세요.
revelation20 2021-06-15 00:37
좋은 강의 감사합니다.
운영자2015-12-29 10:27
안녕하세요. KOCW운영팀입니다. 주 교재는 Introduction to Probability, D.Bertsekas, J.Tsitsklis, hena Scientific 입니다.
caesar700 2015-12-24 15:49
교재가 무엇인가요?

#### 이용방법

• 플래쉬 유형 강의 이용시 필요한 프로그램 [바로가기]

※ 강의별로 교수님의 사정에 따라 전체 차시 중 일부 차시만 공개되는 경우가 있으니 양해 부탁드립니다.