# ## 주메뉴

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• 경북대학교
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공학 >전기ㆍ전자 >전기공학
• 강의학기
2016년 1학기
• 조회수
5,309
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강의계획서 This course provides a broad introduction to probability theory and random processes and their applications to engineering problems.

#### 차시별 강의      1 1. Sample Space and Probability Set theory, Sample Space, Probability Axioms, Some Consequences of the Axioms. 2 2. Discrete Random Variables Discrete Random Variables: PMF, CDF, Expectation, Variance and Standard Deviation, Joint PMF, Conditioning, Independence. 3 3. Continuous Random Variables General Random Variables: CDF, PDF, and Gaussian RV's, Joint PDF, Continuous Conditioning. 4 4. Further Topics on Random Variables Further Topics on Random Variables: Derived Random Variables, Covariance and Correlation, Conditional Expectation and Variance, Transforms, Sums of Independent Random Variables. 5 5. Limit Theorems Limit Theorems: Markov and Chebyshev Inequalities, The Weak Law of Large Numbers, Convergence in Probability, The Central Limit Theorem. 6 6. The Bernoulli and Poisson Processes Stochastic Processes: Bernoulli and Poisson Random Processes. 7 7. Bayesian Statistical Inference Bayesian Statistical Inference: Point Estimation, Hyothesis Testing, MAP, Least Mean Square Estimation. 8 8. Classical Statistical Inference Non-Bayesian Statistical Inference: Linear Regression, Binary Hypothesis Testing, Significance Testing. 9 9. Classical Statistical Inference Non-Bayesian Statistical Inference: Linear Regression, Binary Hypothesis Testing, Significance Testing. #### 연관 자료 #### 사용자 의견 #### 이용방법

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