확률 및 통계학

건국대학교
이향원

페이스북 트위터 카카오톡 네이버밴드 링크복사

주제분류

자연과학 >수학ㆍ물리ㆍ천문ㆍ지리 >통계학
강의학기

2014년 2학기

조회수

53,448
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.

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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